T cell balance gene expression, compositions of matters and methods of use thereof

ABSTRACT

This invention relates generally to compositions and methods for identifying the regulatory network that modulates, controls or otherwise influences T cell balance, for example, Th17 cell differentiation, maintenance and/or function, as well compositions and methods for exploiting the regulatory network that modulates, controls or otherwise influences T cell balance in a variety of therapeutic and/or diagnostic indications. This invention also relates generally to identifying and exploiting target genes and/or target gene products that modulate, control or otherwise influence T cell balance in a variety of therapeutic and/or diagnostic indications.

RELATED APPLICATIONS AND INCORPORATION BY REFERENCE

This application is a is a continuation of prior U.S. patent applicationSer. No. 15/245,748 filed Aug. 24, 2016, which is a continuation-in-partof International Application Number PCT/US15/17826 filed on Feb. 26,2015, which published as PCT Publication Number WO2015/130968 on Sep. 3,2015 and, which claims priority from U.S. Provisional Patent Application61/945,641, filed Feb. 27, 2014, incorporated herein by reference.Reference is made to WO/2012/048265; WO/2014/145631; WO/2014/134351. Theforegoing applications, and all documents cited therein or duringprosecution (“appln cited documents”) and all documents cited orreferenced in the appln cited documents, and all documents cited orreferenced herein (“herein cited documents”), and all documents cited orreferenced in herein cited documents, together with any manufacturer'sinstructions, descriptions, product specifications, and product sheetsfor any products mentioned herein or in any document incorporated byreference herein, are hereby incorporated herein by reference, and maybe employed in the practice of the invention. Appln cited documents,herein cited documents, all documents herein referenced or cited, andall documents indicated to be incorporated herein by reference, areincorporated by reference to the same extent as if each individualdocument was specifically and individually set forth herein in full andindicated to be incorporated by reference when or where cited orreferenced.

STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

This invention was made with government support under Grant NumbersOD003958; HG006193; HG005062; OD003893; NS030843; NS045937; AI073748;AI045757; and AI056299 awarded by the National Institutes of Health. Thegovernment has certain rights in the invention.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has beensubmitted electronically in ASCII format and is hereby incorporated byreferences in its entirety. Said ASCII copy, created on Aug. 23, 2016 isnamed 46783992100_SL.txt and is 324,708 bytes in size.

FIELD OF THE INVENTION

This invention relates generally to compositions and methods foridentifying the regulatory network that modulates, controls or otherwiseinfluences T cell balance, for example, Th17 cell differentiation,maintenance and/or function, as well compositions and methods forexploiting the regulatory network that modulates, controls or otherwiseinfluences T cell balance in a variety of therapeutic and/or diagnosticindications. This invention also relates generally to identifying andexploiting target genes and/or target gene products that modulate,control or otherwise influence T cell balance in a variety oftherapeutic and/or diagnostic indications.

BACKGROUND OF THE INVENTION

Despite their importance, the molecular circuits that control thebalance of T cells, including the differentiation of naïve T cells,remain largely unknown. Recent studies that reconstructed regulatorynetworks in mammalian cells have focused on short-term responses andrelied on perturbation-based approaches that cannot be readily appliedto primary T cells. Accordingly, there exists a need for a betterunderstanding of the dynamic regulatory network that modulates,controls, or otherwise influences T cell balance, including Th17 celldifferentiation, maintenance and function, and means for exploiting thisnetwork in a variety of therapeutic and diagnostic methods. Citationsherein are not intended as an admission that anything cited is pertinentor prior art; nor does it constitute any admission as to the contents ordate of anything cited.

SUMMARY OF THE INVENTION

The invention has many utilities. The invention pertains to and includesmethods and compositions therefrom of Drug Discovery, as well as fordetecting patients or subjects who may or may not respond or beresponding to a particular treatment, therapy, compound, drug orcombination of drugs or compounds; and accordingly ascertaining whichdrug or combination of drugs may provide a particular treatment ortherapy as to a condition or disease or infection or infectious state,as well as methods and compositions for selecting patient populations(e.g., by detecting those who may or may not respond or be responding),or methods and compositions involving personalized treatment—acombination of Drug Discovery and detecting patients or subjects who maynot respond or be responding to a particular treatment, therapy,compound, drug or combination of drugs or compounds (e.g., by as toindividual(s), so detecting response, nor responding, potential torespond or not, and adjusting particular treatment, therapy, compound,drug or combination of drugs or compounds to be administered oradministering a treatment, therapy, compound, drug or combination ofdrugs or compounds indicated from the detecting).

The invention provides compositions and methods for modulating T cellbalance, e.g., Th17 cell differentiation, maintenance and function, andmeans for exploiting this network in a variety of therapeutic anddiagnostic methods. As used herein, the term “modulating” includesup-regulation of, or otherwise increasing, the expression of one or moregenes, down-regulation of, or otherwise decreasing, the expression ofone or more genes, inhibiting or otherwise decreasing the expression,activity and/or function of one or more gene products, and/or enhancingor otherwise increasing the expression, activity and/or function of oneor more gene products.

As used herein, the term “modulating T cell balance” includes themodulation of any of a variety of T cell-related functions and/oractivities, including by way of non-limiting example, controlling orotherwise influencing the networks that regulate T cell differentiation;controlling or otherwise influencing the networks that regulate T cellmaintenance, for example, over the lifespan of a T cell; controlling orotherwise influencing the networks that regulate T cell function;controlling or otherwise influencing the networks that regulate helper Tcell (Th cell) differentiation; controlling or otherwise influencing thenetworks that regulate Th cell maintenance, for example, over thelifespan of a Th cell; controlling or otherwise influencing the networksthat regulate Th cell function; controlling or otherwise influencing thenetworks that regulate Th17 cell differentiation; controlling orotherwise influencing the networks that regulate Th17 cell maintenance,for example, over the lifespan of a Th17 cell; controlling or otherwiseinfluencing the networks that regulate Th17 cell function; controllingor otherwise influencing the networks that regulate regulatory T cell(Treg) differentiation; controlling or otherwise influencing thenetworks that regulate Treg cell maintenance, for example, over thelifespan of a Treg cell; controlling or otherwise influencing thenetworks that regulate Treg cell function; controlling or otherwiseinfluencing the networks that regulate other CD4+ T celldifferentiation; controlling or otherwise influencing the networks thatregulate other CD4+ T cell maintenance; controlling or otherwiseinfluencing the networks that regulate other CD4+ T cell function;manipulating or otherwise influencing the ratio of T cells such as, forexample, manipulating or otherwise influencing the ratio of Th17 cellsto other T cell types such as Tregs or other CD4+ T cells; manipulatingor otherwise influencing the ratio of different types of Th17 cells suchas, for example, pathogenic Th17 cells and non-pathogenic Th17 cells;manipulating or otherwise influencing at least one function orbiological activity of a T cell; manipulating or otherwise influencingat least one function or biological activity of Th cell; manipulating orotherwise influencing at least one function or biological activity of aTreg cell; manipulating or otherwise influencing at least one functionor biological activity of a Th17 cell; and/or manipulating or otherwiseinfluencing at least one function or biological activity of another CD4+T cell.

The invention provides T cell modulating agents that modulate T cellbalance. For example, in some embodiments, the invention provides T cellmodulating agents and methods of using these T cell modulating agents toregulate, influence or otherwise impact the level(s) of and/or balancebetween T cell types, e.g., between Th17 and other T cell types, forexample, regulatory T cells (Tregs), and/or Th17 activity andinflammatory potential. As used herein, terms such as “Th17 cell” and/or“Th17 phenotype” and all grammatical variations thereof refer to adifferentiated T helper cell that expresses one or more cytokinesselected from the group the consisting of interleukin 17A (IL-17A),interleukin 17F (IL-17F), and interleukin 17A/F heterodimer (IL17-AF).As used herein, terms such as “Th1 cell” and/or “Th1 phenotype” and allgrammatical variations thereof refer to a differentiated T helper cellthat expresses interferon gamma (IFNγ). As used herein, terms such as“Th2 cell” and/or “Th2 phenotype” and all grammatical variations thereofrefer to a differentiated T helper cell that expresses one or morecytokines selected from the group the consisting of interleukin 4(IL-4), interleukin 5 (IL-5) and interleukin 13 (IL-13). As used herein,terms such as “Treg cell” and/or “Treg phenotype” and all grammaticalvariations thereof refer to a differentiated T cell that expressesFoxp3.

For example, in some embodiments, the invention provides T cellmodulating agents and methods of using these T cell modulating agents toregulate, influence or otherwise impact the level of and/or balancebetween Th17 phenotypes, and/or Th17 activity and inflammatorypotential. Suitable T cell modulating agents include an antibody, asoluble polypeptide, a polypeptide agent, a peptide agent, a nucleicacid agent, a nucleic acid ligand, or a small molecule agent.

For example, in some embodiments, the invention provides T cellmodulating agents and methods of using these T cell modulating agents toregulate, influence or otherwise impact the level of and/or balancebetween Th17 cell types, e.g., between pathogenic and non-pathogenicTh17 cells. For example, in some embodiments, the invention provides Tcell modulating agents and methods of using these T cell modulatingagents to regulate, influence or otherwise impact the level of and/orbalance between pathogenic and non-pathogenic Th17 activity.

For example, in some embodiments, the invention provides T cellmodulating agents and methods of using these T cell modulating agents toinfluence or otherwise impact the differentiation of a population of Tcells, for example toward Th17 cells, with or without a specificpathogenic distinction, or away from Th17 cells, with or without aspecific pathogenic distinction

For example, in some embodiments, the invention provides T cellmodulating agents and methods of using these T cell modulating agents toinfluence or otherwise impact the differentiation of a population of Tcells, for example toward a non-Th17 T cell subset or away from anon-Th17 cell subset. For example, in some embodiments, the inventionprovides T cell modulating agents and methods of using these T cellmodulating agents to induce T-cell plasticity, i.e., converting Th17cells into a different subtype, or into a new state.

For example, in some embodiments, the invention provides T cellmodulating agents and methods of using these T cell modulating agents toinduce T cell plasticity, e.g., converting Th17 cells into a differentsubtype, or into a new state.

For example, in some embodiments, the invention provides T cellmodulating agents and methods of using these T cell modulating agents toachieve any combination of the above.

In some embodiments, the T cells are naïve T cells. In some embodiments,the T cells are differentiated T cells. In some embodiments, the T cellsare partially differentiated T cells. In some embodiments, the T cellsare a mixture of naïve T cells and differentiated T cells. In someembodiments, the T cells are mixture of naïve T cells and partiallydifferentiated T cells. In some embodiments, the T cells are mixture ofpartially differentiated T cells and differentiated T cells. In someembodiments, the T cells are mixture of naïve T cells, partiallydifferentiated T cells, and differentiated T cells.

The T cell modulating agents are used to modulate the expression of oneor more target genes or one or more products of one or more target genesthat have been identified as genes responsive to Th17-relatedperturbations. These target genes are identified, for example,contacting a T cell, e.g., naïve T cells, partially differentiated Tcells, differentiated T cells and/or combinations thereof, with a T cellmodulating agent and monitoring the effect, if any, on the expression ofone or more signature genes or one or more products of one or moresignature genes. In some embodiments, the one or more signature genesare selected from those listed in Table 1 or Table 2 of thespecification.

In some embodiments, the target gene is one or more Th17-associatedcytokine(s) or receptor molecule(s) selected from those listed in Table3 of the specification. In some embodiments, the target gene is one ormore Th17-associated transcription regulator(s) selected from thoseshown in Table 4 of the specification.

In some embodiments, the target gene is one or more Th17-associatedtranscription regulator(s) selected from those shown in Table 5 of thespecification. In some embodiments, the target gene is one or moreTh17-associated receptor molecule(s) selected from those listed in Table6 of the specification. In some embodiments, the target gene is one ormore Th17-associated kinase(s) selected from those listed in Table 7 ofthe specification. In some embodiments, the target gene is one or moreTh17-associated signaling molecule(s) selected from those listed inTable 8 of the specification. In some embodiments, the target gene isone or more Th17-associated receptor molecule(s) selected from thoselisted in Table 9 of the specification.

In some embodiments, the target gene is one or more target genesinvolved in induction of Th17 differentiation such as, for example,IRF1, IRF8, IRF9, STAT2, STAT3, IRF7, STAT1, ZFP281, IFI35, REL, TBX21,FLI1, BATF, IRF4, one or more of the target genes listed in Table 5 asbeing associated with the early stage of Th17 differentiation,maintenance and/or function, e.g., AES, AHR, ARID5A, BATF, BCL11B, BCL3,CBFB, CBX4, CHD7, CITED2, CREB1, E2F4, EGR1, EGR2, ELL2, ETS1, ETS2,ETV6, EZH1, FLI1, FOXO1, GATA3, GATAD2B, HIF1A, ID2, IFI35, IKZF4, IRF1,IRF2, IRF3, IRF4, IRF7, IRF9, JMJD1C, JUN, LEF1, LRRFIP1, MAX, NCOA3,NFE2L2, NFIL3, NFKB1, NMI, NOTCH1, NR3C1, PHF21A, PML, PRDM1, REL, RELA,RUNX1, SAP18, SATB1, SMAD2, SMARCA4, SP100, SP4, STAT1, STAT2, STAT3,STAT4, STAT5B, STAT6, TFEB, TP53, TRIM24, and/or ZFP161, or anycombination thereof.

In some embodiments, the target gene is one or more target genesinvolved in onset of Th17 phenotype and amplification of Th17 T cellssuch as, for example, IRF8, STAT2, STAT3, IRF7, JUN, STAT5B, ZPF2981,CHD7, TBX21, FLI1, SATB1, RUNX1, BATF, RORC, SP4, one or more of thetarget genes listed in Table 5 as being associated with the intermediatestage of Th17 differentiation, maintenance and/or function, e.g., AES,AHR, ARID3A, ARID5A, ARNTL, ASXL1, BATF, BCL11B, BCL3, BCL6, CBFB, CBX4,CDC5L, CEBPB, CHD7, CREB1, CREB3L2, CREM, E2F4, E2F8, EGR1, EGR2, ELK3,ELL2, ETS1, ETS2, ETV6, EZH1, FLI1, FOSL2, FOXJ2, FOXO1, FUS, HIF1A,HMGB2, ID1, ID2, IFI35, IKZF4, IRF3, IRF4, IRF7, IRF8, IRF9, JUN, JUNB,KAT2B, KLF10, KLF6, KLF9, LEF1, LRRFIP1, MAFF, MAX, MAZ, MINA, MTA3,MYC, MYST4, NCOA1, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCH1, NR3C1,PHF21A, PML, POU2AF1, POU2F2, PRDM1, RARA, RBPJ, RELA, RORA, RUNX1,SAP18, SATB1, SKI, SKIL, SMAD2, SMAD7, SMARCA4, SMOX, SP1, SP4, SS18,STAT1, STAT2, STAT3, STAT5A, STAT5B, STAT6, SUZ12, TBX21, TFEB, TLE1,TP53, TRIM24, TRIM28, TRPS1, VAV1, ZEB1, ZEB2, ZFP161, ZFP62, ZNF238,ZNF281, and/or ZNF703, or any combination thereof.

In some embodiments, the target gene is one or more target genesinvolved in stabilization of Th17 cells and/or modulatingTh17-associated interleukin 23 (IL-23) signaling such as, for example,STAT2, STAT3, JUN, STAT5B, CHD7, SATB1, RUNX1, BATF, RORC, SP4 IRF4, oneor more of the target genes listed in Table 5 as being associated withthe late stage of Th17 differentiation, maintenance and/or function,e.g., AES, AHR, ARID3A, ARID5A, ARNTL, ASXL1, ATF3, ATF4, BATF, BATF3,BCL11B, BCL3, BCL6, C21ORF66, CBFB, CBX4, CDC5L, CDYL, CEBPB, CHD7,CHMP1B, CIC, CITED2, CREB1, CREB3L2, CREM, CSDA, DDIT3, E2F1, E2F4,E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, EZH1, FLI1, FOSL2, FOXJ2,FOXO1, FUS, GATA3, GATAD2B, HCLS1, HIF1A, ID1, ID2, IFI35, IKZF4, IRF3,IRF4, IRF7, IRF8, IRF9, JARID2, JMJD1C, JUN, JUNB, KAT2B, KLF10, KLF6,KLF7, KLF9, LASS4, LEF1, LRRFIP1, MAFF, MAX, MEN1, MINA, MTA3, MXI1,MYC, MYST4, NCOA1, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCH1, NR3C1,PHF13, PHF21A, PML, POU2AF1, POU2F2, PRDM1, RARA, RBPJ, REL, RELA,RNF11, RORA, RORC, RUNX1, RUNX2, SAP18, SAP30, SATB1, SERTAD1, SIRT2,SKI, SKIL, SMAD2, SMAD4, SMAD7, SMARCA4, SMOX, SP1, SP100, SP4, SS18,STAT1, STAT3, STAT4, STAT5A, STAT5B, STATE, SUZ12, TBX21, TFEB, TGIF1,TLE1, TP53, TRIM24, TRPS1, TSC22D3, UBE2B, VAV1, VAX2, XBP1, ZEB1, ZEB2,ZFP161, ZFP36L1, ZFP36L2, ZNF238, ZNF281, ZNF703, ZNRF1, and/or ZNRF2,or any combination thereof.

In some embodiments, the target gene is one or more of the target geneslisted in Table 6 as being associated with the early stage of Th17differentiation, maintenance and/or function, e.g., FAS, CCR5, IL6ST,IL17RA, IL2RA, MYD88, CXCR5, PVR, IL15RA, IL12RB1, or any combinationthereof.

In some embodiments, the target gene is one or more of the target geneslisted in Table 6 as being associated with the intermediate stage ofTh17 differentiation, maintenance and/or function, e.g., IL7R, ITGA3,IL1R1, CCR5, CCR6, ACVR2A, IL6ST, IL17RA, CCR8, DDR1, PROCR, IL2RA,IL12RB2, MYD88, PTPRJ, TNFRSF13B, CXCR3, IL1RN, CXCR5, CCR4, IL4R,IL2RB, TNFRSF12A, CXCR4, KLRD1, IRAK1BP1, PVR, IL12RB1, IL18R1, TRAF3,or any combination thereof.

In some embodiments, the target gene is one or more of the target geneslisted in Table 6 as being associated with the late stage of Th17differentiation, maintenance and/or function, e.g., IL7R, ITGA3, IL1R1,FAS, CCR5, CCR6, ACVR2A, IL6ST, IL17RA, DDR1, PROCR, IL2RA, IL12RB2,MYD88, BMPR1A, PTPRJ, TNFRSF13B, CXCR3, IL1RN, CXCR5, CCR4, IL4R, IL2RB,TNFRSF12A, CXCR4, KLRD1, IRAK1BP1, PVR, IL15RA, TLR1, ACVR1B, IL12RB1,IL18R1, TRAF3, IFNGR1, PLAUR, IL21R, IL23R, or any combination thereof.

In some embodiments, the target gene is one or more of the target geneslisted in Table 7 as being associated with the early stage of Th17differentiation, maintenance and/or function, e.g., EIF2AK2, DUSP22,HK2, RIPK1, RNASEL, TEC, MAP3K8, SGK1, PRKCQ, DUSP16, BMP2K, PIM2, orany combination thereof.

In some embodiments, the target gene is one or more of the target geneslisted in Table 7 as being associated with the intermediate stage ofTh17 differentiation, maintenance and/or function, e.g., PSTPIP1, PTPN1,ACP5, TXK, RIPK3, PTPRF, NEK4, PPME1, PHACTR2, HK2, GMFG, DAPP1, TEC,GMFB, PIM1, NEK6, ACVR2A, FES, CDK6, ZAK, DU5P14, SGK1, JAK3, ULK2,PTPRJ, SPHK1, TNK2, PCTK1, MAP4K3, TGFBR1, HK1, DDR1, BMP2K, DUSP10,ALPK2, or any combination thereof.

In some embodiments, the target gene is one or more of the target geneslisted in Table 7 as being associated with the late stage of Th17differentiation, maintenance and/or function, e.g., PTPLA, PSTPIP1, TK1,PTEN, BPGM, DCK, PTPRS, PTPN18, MKNK2, PTPN1, PTPRE, SH2D1A, PLK2,DUSP6, CDC25B, SLK, MAP3K5, BMPR1A, ACP5, TXK, RIPK3, PPP3CA, PTPRF,PACSIN1, NEK4, PIP4K2A, PPME1, SRPK2, DUSP2, PHACTR2, DCLK1, PPP2R5A,RIPK1, GK, RNASEL, GMFG, STK4, HINT3, DAPP1, TEC, GMFB, PTPN6, RIPK2,PIM1, NEK6, ACVR2A, AURKB, FES, ACVR1B, CDK6, ZAK, VRK2, MAP3K8, DUSP14,SGK1, PRKCQ, JAK3, ULK2, HIPK2, PTPRJ, INPP1, TNK2, PCTK1, DUSP1, NUDT4,TGFBR1, PTP4A1, HK1, DUSP16, ANP32A, DDR1, ITK, WNK1, NAGK, STK38,BMP2K, BUB1, AAK1, SIK1, DUSP10, PRKCA, PIM2, STK17B, TK2, STK39, ALPK2,MST4, PHLPP1, or any combination thereof.

In some embodiments, the target gene is one or more of the target geneslisted in Table 8 as being associated with the early stage of Th17differentiation, maintenance and/or function, e.g., HK2, CDKN1A, DUT,DUSP1, NADK, LIMK2, DUSP11, TAOK3, PRPS1, PPP2R4, MKNK2, SGK1, BPGM,TEC, MAPK6, PTP4A2, PRPF4B, ACP1, CCRN4L, or any combination thereof.

In some embodiments, the target gene is one or more of the target geneslisted in Table 8 as being associated with the intermediate stage ofTh17 differentiation, maintenance and/or function, e.g., HK2, ZAP70,NEK6, DUSP14, SH2D1A, ITK, DUT, PPP1R11, DUSP1, PMVK, TK1, TAOK3, GMFG,PRPS1, SGK1, TXK, WNK1, DUSP19, TEC, RPS6KA1, PKM2, PRPF4B, ADRBK1, CKB,ULK2, PLK1, PPP2R5A, PLK2, or any combination thereof.

In some embodiments, the target gene is one or more of the target geneslisted in Table 8 as being associated with the late stage of Th17differentiation, maintenance and/or function, e.g., ZAP70, PFKP, NEK6,DUSP14, SH2D1A, INPP5B, ITK, PFKL, PGK1, CDKN1A, DUT, PPP1R11, DUSP1,PMVK, PTPN22, PSPH, TK1, PGAM1, LIMK2, CLK1, DUSP11, TAOK3, RIOK2, GMFG,UCKL1, PRPS1, PPP2R4, MKNK2, DGKA, SGK1, TXK, WNK1, DUSP19, CHP, BPGM,PIP5K1A, TEC, MAP2K1, MAPK6, RPS6KA1, PTP4A2, PKM2, PRPF4B, ADRBK1, CKB,ACP1, ULK2, CCRN4L, PRKCH, PLK1, PPP2R5A, PLK2, or any combinationthereof.

In some embodiments, the target gene is one or more of the target geneslisted in Table 9 as being associated with the early stage of Th17differentiation, maintenance and/or function, e.g., CD200, CD40LG, CD24,CCND2, ADAM17, BSG, ITGAL, FAS, GPR65, SIGMAR1, CAP1, PLAUR, SRPRB,TRPV2, IL2RA, KDELR2, TNFRSF9, or any combination thereof.

In some embodiments, the target gene is one or more of the target geneslisted in Table 9 as being associated with the intermediate stage ofTh17 differentiation, maintenance and/or function, e.g., CTLA4, CD200,CD24, CD5L, CD9, IL2RB, CD53, CD74, CAST, CCR6, IL2RG, ITGAV, FAS, IL4R,PROCR, GPR65, TNFRSF18, RORA, IL1RN, RORC, CYSLTR1, PNRC2, LOC390243,ADAM10, TNFSF9, CD96, CD82, SLAMF7, CD27, PGRMC1, TRPV2, ADRBK1, TRAF6,IL2RA, THY1, IL12RB2, TNFRSF9, or any combination thereof.

In some embodiments, the target gene is one or more of the target geneslisted in Table 9 as being associated with the late stage of Th17differentiation, maintenance and/or function, e.g., CTLA4, TNFRSF4,CD44, PDCD1, CD200, CD247, CD24, CD5L, CCND2, CD9, IL2RB, CD53, CD74,ADAM17, BSG, CAST, CCR6, IL2RG, CD81, CD6, CD48, ITGAV, TFRC, ICAM2,ATP1B3, FAS, IL4R, CCR7, CD52, PROCR, GPR65, TNFRSF18, FCRL1, RORA,IL1RN, RORC, P2RX4, SSR2, PTPN22, SIGMAR1, CYSLTR1, LOC390243, ADAM10,TNFSF9, CD96, CAP1, CD82, SLAMF7, PLAUR, CD27, SIVA1, PGRMC1, SRPRB,TRPV2, NR1H2, ADRBK1, GABARAPL1, TRAF6, IL2RA, THY1, KDELR2, IL12RB2,TNFRSF9, SCARB1, IFNGR1, or any combination thereof.

In some embodiments, the target gene is one or more target genes that isa promoter of Th17 cell differentiation. In some embodiments, the targetgene is GPR65. In some embodiments, the target gene is also a promoterof pathogenic Th17 cell differentiation and is selected from the groupconsisting of CD5L, DEC1, PLZP and TCF4.

In some embodiments, the target gene is one or more target genes that isa promoter of pathogenic Th17 cell differentiation. In some embodiments,the target gene is selected from the group consisting of CD5L, DEC1,PLZP and TCF4.

The desired gene or combination of target genes is selected, and afterdetermining whether the selected target gene(s) is overexpressed orunder-expressed during Th17 differentiation and/or Th17 maintenance, asuitable antagonist or agonist is used depending on the desireddifferentiation, maintenance and/or function outcome. For example, fortarget genes that are identified as positive regulators of Th17differentiation, use of an antagonist that interacts with those targetgenes will shift differentiation away from the Th17 phenotype, while useof an agonist that interacts with those target genes will shiftdifferentiation toward the Th17 phenotype. For target genes that areidentified as negative regulators of Th17 differentiation, use of anantagonist that interacts with those target genes will shiftdifferentiation toward from the Th17 phenotype, while use of an agonistthat interacts with those target genes will shift differentiation awaythe Th17 phenotype. For example, for target genes that are identified aspositive regulators of Th17 maintenance, use of an antagonist thatinteracts with those target genes will reduce the number of cells withthe Th17 phenotype, while use of an agonist that interacts with thosetarget genes will increase the number of cells with the Th17 phenotype.For target genes that are identified as negative regulators of Th17differentiation, use of an antagonist that interacts with those targetgenes will increase the number of cells with the Th17 phenotype, whileuse of an agonist that interacts with those target genes will reduce thenumber of cells with the Th17 phenotype. Suitable T cell modulatingagents include an antibody, a soluble polypeptide, a polypeptide agent,a peptide agent, a nucleic acid agent, a nucleic acid ligand, or a smallmolecule agent.

In some embodiments, the positive regulator of Th17 differentiation is atarget gene selected from MINA, TRPS1, MYC, NKFB1, NOTCH, PML, POU2AF1,PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1, EGR2, ETV6, FAS,IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3, and combinations thereof. Insome embodiments, the positive regulator of Th17 differentiation is atarget gene selected from MINA, PML, POU2AF1, PROCR, SMARCA4, ZEB1,EGR2, CCR6, FAS and combinations thereof.

In some embodiments, the negative regulator of Th17 differentiation is atarget gene selected from SP4, ETS2, IKZF4, TSC22D3, IRF1 andcombinations thereof. In some embodiments, the negative regulator ofTh17 differentiation is a target gene selected from SP4, IKZF4, TSC22D3and combinations thereof.

In some embodiments, the T cell modulating agent is a soluble Faspolypeptide or a polypeptide derived from FAS. In some embodiments, theT cell modulating agent is an agent that enhances or otherwise increasesthe expression, activity, and/or function of FAS in Th17 cells. As shownherein, expression of FAS in T cell populations induced or otherwiseinfluenced differentiation toward Th17 cells. In some embodiments, theseT cell modulating agents are useful in the treatment of an immuneresponse, for example, an autoimmune response or an inflammatoryresponse. In some embodiments, these T cell modulating agents are usefulin the treatment of an infectious disease or other pathogen-baseddisorders. In some embodiments, the T cell modulating agent is anantibody, a soluble polypeptide, a polypeptide agonist, a peptideagonist, a nucleic acid agonist, a nucleic acid ligand, or a smallmolecule agonist. In some embodiments, the T cells are naïve T cells. Insome embodiments, the T cells are differentiated T cells. In someembodiments, the T cells are partially differentiated T cells. In someembodiments, the T cells are a mixture of naïve T cells anddifferentiated T cells. In some embodiments, the T cells are mixture ofnaïve T cells and partially differentiated T cells. In some embodiments,the T cells are mixture of partially differentiated T cells anddifferentiated T cells. In some embodiments, the T cells are mixture ofnaïve T cells, partially differentiated T cells, and differentiated Tcells.

In some embodiments, the T cell modulating agent is an agent thatinhibits the expression, activity and/or function of FAS. Inhibition ofFAS expression, activity and/or function in T cell populations repressedor otherwise influenced differentiation away from Th17 cells and/orinduced or otherwise influenced differentiation toward regulatory Tcells (Tregs) and towards Th1 cells. In some embodiments, these T cellmodulating agents are useful in the treatment of an immune response, forexample, an autoimmune response or an inflammatory response. In someembodiments, these T cell modulating agents are useful in the treatmentof autoimmune diseases such as psoriasis, inflammatory bowel disease(IBD), ankylosing spondylitis, multiple sclerosis, Sjögren's syndrome,uveitis, and rheumatoid arthritis, asthma, systemic lupus erythematosus,transplant rejection including allograft rejection, and combinationsthereof. In addition, enhancement of Th17 cells is also useful forclearing fungal infections and extracellular pathogens. In someembodiments, the T cell modulating agent is an antibody, a solublepolypeptide, a polypeptide antagonist, a peptide antagonist, a nucleicacid antagonist, a nucleic acid ligand, or a small molecule antagonist.In some embodiments, the T cells are naïve T cells. In some embodiments,the T cells are differentiated T cells. In some embodiments, the T cellsare partially differentiated T cells that express additional cytokines.In some embodiments, the T cells are a mixture of naïve T cells anddifferentiated T cells. In some embodiments, the T cells are mixture ofnaïve T cells and partially differentiated T cells. In some embodiments,the T cells are mixture of partially differentiated T cells anddifferentiated T cells. In some embodiments, the T cells are mixture ofnaïve T cells, partially differentiated T cells, and differentiated Tcells.

In some embodiments, the T cell modulating agent is an agent thatinhibits the expression, activity and/or function of CCR5. Inhibition ofCCR5 expression, activity and/or function in T cell populationsrepressed or otherwise influenced differentiation away from Th17 cellsand/or induced or otherwise influenced differentiation toward regulatoryT cells (Tregs) and towards Th1 cells. In some embodiments, these T cellmodulating agents are useful in the treatment of an immune response, forexample, an autoimmune response or an inflammatory response. In someembodiments, the T cell modulating agent is an inhibitor or neutralizingagent. In some embodiments, the T cell modulating agent is an antibody,a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, anucleic acid antagonist, a nucleic acid ligand, or a small moleculeantagonist. In some embodiments, the T cells are naïve T cells. In someembodiments, the T cells are differentiated T cells. In someembodiments, the T cells are partially differentiated T cells. In someembodiments, the T cells are a mixture of naïve T cells anddifferentiated T cells. In some embodiments, the T cells are mixture ofnaïve T cells and partially differentiated T cells. In some embodiments,the T cells are mixture of partially differentiated T cells anddifferentiated T cells. In some embodiments, the T cells are mixture ofnaïve T cells, partially differentiated T cells, and differentiated Tcells.

In some embodiments, the T cell modulating agent is an agent thatinhibits the expression, activity and/or function of CCR6. Inhibition ofCCR6 expression, activity and/or function in T cell populationsrepressed or otherwise influenced differentiation away from Th17 cellsand/or induced or otherwise influenced differentiation toward regulatoryT cells (Tregs) and towards Th1 cells. In some embodiments, these T cellmodulating agents are useful in the treatment of an immune response, forexample, an autoimmune response or an inflammatory response. In someembodiments, the T cell modulating agent is an antibody, a solublepolypeptide, a polypeptide antagonist, a peptide antagonist, a nucleicacid antagonist, a nucleic acid ligand, or a small molecule antagonist.In some embodiments, the T cells are naïve T cells. In some embodiments,the T cells are differentiated T cells. In some embodiments, the T cellsare partially differentiated T cells. In some embodiments, the T cellsare a mixture of naïve T cells and differentiated T cells. In someembodiments, the T cells are mixture of naïve T cells and partiallydifferentiated T cells. In some embodiments, the T cells are mixture ofpartially differentiated T cells and differentiated T cells. In someembodiments, the T cells are mixture of naïve T cells, partiallydifferentiated T cells, and differentiated T cells.

In some embodiments, the T cell modulating agent is an agent thatinhibits the expression, activity and/or function of EGR1. Inhibition ofEGR1 expression, activity and/or function in T cell populationsrepressed or otherwise influenced differentiation away from Th17 cellsand/or induced or otherwise influenced differentiation toward regulatoryT cells (Tregs) and towards Th1 cells. In some embodiments, these T cellmodulating agents are useful in the treatment of an immune response, forexample, an autoimmune response or an inflammatory response. In someembodiments, the T cell modulating agent is an antibody, a solublepolypeptide, a polypeptide antagonist, a peptide antagonist, a nucleicacid antagonist, a nucleic acid ligand, or a small molecule antagonist.In some embodiments, the T cells are naïve T cells. In some embodiments,the T cells are differentiated T cells. In some embodiments, the T cellsare partially differentiated T cells. In some embodiments, the T cellsare a mixture of naïve T cells and differentiated T cells. In someembodiments, the T cells are mixture of naïve T cells and partiallydifferentiated T cells. In some embodiments, the T cells are mixture ofpartially differentiated T cells and differentiated T cells. In someembodiments, the T cells are mixture of naïve T cells, partiallydifferentiated T cells, and differentiated T cells.

In some embodiments, the T cell modulating agent is an agent thatinhibits the expression, activity and/or function of EGR2. Inhibition ofEGR2 expression, activity and/or function in T cell populationsrepressed or otherwise influenced differentiation away from Th17 cellsand/or induced or otherwise influenced differentiation toward regulatoryT cells (Tregs) and towards Th1 cells. In some embodiments, these T cellmodulating agents are useful in the treatment of an immune response, forexample, an autoimmune response or an inflammatory response. In someembodiments, the T cell modulating agent is an antibody, a solublepolypeptide, a polypeptide antagonist, a peptide antagonist, a nucleicacid antagonist, a nucleic acid ligand, or a small molecule antagonist.In some embodiments, the T cells are naïve T cells. In some embodiments,the T cells are differentiated T cells. In some embodiments, the T cellsare partially differentiated T cells. In some embodiments, the T cellsare a mixture of naïve T cells and differentiated T cells. In someembodiments, the T cells are mixture of naïve T cells and partiallydifferentiated T cells. In some embodiments, the T cells are mixture ofpartially differentiated T cells and differentiated T cells. In someembodiments, the T cells are mixture of naïve T cells, partiallydifferentiated T cells, and differentiated T cells.

For example, in some embodiments, the invention provides T cellmodulating agents and methods of using these T cell modulating agents toregulate, influence or otherwise impact the phenotype of a Th17 cell orpopulation of cells, for example, by influencing a naïve T cell orpopulation of cells to differentiate to a pathogenic or non-pathogenicTh17 cell or population of cells, by causing a pathogenic Th17 cell orpopulation of cells to switch to a non-pathogenic Th17 cell orpopulation of T cells (e.g., populations of naïve T cells, partiallydifferentiated T cells, differentiated T cells and combinationsthereof), or by causing a non-pathogenic Th17 cell or population of Tcells (e.g., populations of naïve T cells, partially differentiated Tcells, differentiated T cells and combinations thereof) to switch to apathogenic Th17 cell or population of cells.

In some embodiments, the invention comprises a method of drug discoveryfor the treatment of a disease or condition involving an immune responseinvolving T cell balance in a population of cells or tissue of a targetgene comprising the steps of providing a compound or plurality ofcompounds to be screened for their efficacy in the treatment of saiddisease or condition, contacting said compound or plurality of compoundswith said population of cells or tissue, detecting a first level ofexpression, activity and/or function of a target gene, comparing thedetected level to a control of level of a target gene, and evaluatingthe difference between the detected level and the control level todetermine the immune response elicited by said compound or plurality ofcompounds. For example, the method contemplates comparing tissue sampleswhich can be inter alia infected tissue, inflamed tissue, healthytissue, or combinations of tissue samples thereof.

In one embodiment of the invention, the reductase null animals of thepresent invention may advantageously be used to modulate T cell balancein a tissue or cell specific manner. Such animals may be used for theapplications hereinbefore described, where the role of T cell balance inproduct/drug metabolism, detoxification, normal homeostasis or indisease etiology is to be studied. It is envisaged that this embodimentwill also allow other effects, such as drug transporter-mediatedeffects, to be studied in those tissues or cells in the absence ofmetabolism, e.g., carbon metabolism. Accordingly the animals of thepresent invention, in a further aspect of the invention may be used tomodulate the functions and antibodies in any of the above cell types togenerate a disease model or a model for product/drug discovery or amodel to verify or assess functions of T cell balance

In another embodiment, the method contemplates use of animal tissuesand/or a population of cells derived therefrom of the present inventionas an in vitro assay for the study of any one or more of the followingevents/parameters: (i) role of transporters in product uptake andefflux; (ii) identification of product metabolites produced by T cells;(iii) evaluate whether candidate products are T cells; or (iv) assessdrug/drug interactions due to T cell balance.

The terms “pathogenic” or “non-pathogenic” as used herein are not to beconstrued as implying that one Th17 cell phenotype is more desirablethan the other. As described herein, there are instances in whichinhibiting the induction of pathogenic Th17 cells or modulating the Th17phenotype towards the non-pathogenic Th17 phenotype is desirable.Likewise, there are instances where inhibiting the induction ofnon-pathogenic Th17 cells or modulating the Th17 phenotype towards thepathogenic Th17 phenotype is desirable.

As used herein, terms such as “pathogenic Th17 cell” and/or “pathogenicTh17 phenotype” and all grammatical variations thereof refer to Th17cells that, when induced in the presence of TGF-β3, express an elevatedlevel of one or more genes selected from Cxcl3, IL22, IL3, Ccl4, Gzmb,Lrmp, Ccl5, Casp1, Csf2, Ccl3, Tbx21, Icos, IL17r, Stat4, Lgals3 andLag, as compared to the level of expression in a TGF-β3-induced Th17cells. As used herein, terms such as “non-pathogenic Th17 cell” and/or“non-pathogenic Th17 phenotype” and all grammatical variations thereofrefer to Th17 cells that, when induced in the presence of TGF-β3,express a decreased level of one or more genes selected from IL6st,IL1rn, Ikzf3, Maf, Ahr, IL9 and IL10, as compared to the level ofexpression in a TGF-β3-induced Th17 cells.

In some embodiments, the T cell modulating agent is an agent thatenhances or otherwise increases the expression, activity and/or functionof Protein C Receptor (PROCR, also called EPCR or CD201) in Th17 cells.As shown herein, expression of PROCR in Th17 cells reduced thepathogenicity of the Th17 cells, for example, by switching Th17 cellsfrom a pathogenic to non-pathogenic signature. Thus, PROCR and/or theseagonists of PROCR are useful in the treatment of a variety ofindications, particularly in the treatment of aberrant immune response,for example in autoimmune diseases and/or inflammatory disorders. Insome embodiments, the T cell modulating agent is an antibody, a solublepolypeptide, a polypeptide agonist, a peptide agonist, a nucleic acidagonist, a nucleic acid ligand, or a small molecule agonist.

In some embodiments, the T cell modulating agent is an agent thatinhibits the expression, activity and/or function of the Protein CReceptor (PROCR, also called EPCR or CD201). Inhibition of PROCRexpression, activity and/or function in Th17 cells switchesnon-pathogenic Th17 cells to pathogenic Th17 cells. Thus, these PROCRantagonists are useful in the treatment of a variety of indications, forexample, infectious disease and/or other pathogen-based disorders. Insome embodiments, the T cell modulating agent is an antibody, a solublepolypeptide, a polypeptide antagonist, a peptide antagonist, a nucleicacid antagonist, a nucleic acid ligand, or a small molecule antagonist.In some embodiments, the T cell modulating agent is a soluble Protein CReceptor (PROCR, also called EPCR or CD201) polypeptide or a polypeptidederived from PROCR.

In some embodiments, the invention provides a method of inhibiting Th17differentiation, maintenance and/or function in a cell population and/orincreasing expression, activity and/or function of one or morenon-Th17-associated cytokines, one or more non-Th17 associated receptormolecules, or non-Th17-associated transcription regulators selected fromFOXP3, interferon gamma (IFN-γ), GATA3, STAT4 and TBX21, comprisingcontacting a T cell with an agent that inhibits expression, activityand/or function of MINA, MYC, NKFB1, NOTCH, PML, POU2AF1, PROCR, RBPJ,SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1, EGR2, ETV6, FAS, IL12RB1, IL17RA,IL21R, IRF4, IRF8, ITGA3 or combinations thereof. In some embodiments,the agent inhibits expression, activity and/or function of at least oneof MINA, PML, POU2AF1, PROCR, SMARCA4, ZEB1, EGR2, CCR6, FAS orcombinations thereof. In some embodiments, the agent is an antibody, asoluble polypeptide, a polypeptide antagonist, a peptide antagonist, anucleic acid antagonist, a nucleic acid ligand, or a small moleculeantagonist. In some embodiments, the antibody is a monoclonal antibody.In some embodiments, the antibody is a chimeric, humanized or fullyhuman monoclonal antibody. In some embodiments, the T cell is a naïve Tcell, and wherein the agent is administered in an amount that issufficient to modulate the phenotype of the T cell to become and/orproduce a desired non-Th17 T cell phenotype, for example, a regulatory Tcell (Treg) phenotype or another CD4+ T cell phenotype. In someembodiments, the T cell is a partially differentiated T cell, andwherein the agent is administered in an amount that is sufficient tomodulate the phenotype of the partially differentiated T cell to becomeand/or produce a desired non-Th17 T cell phenotype, for example, aregulatory T cell (Treg) phenotype or another CD4+ T cell phenotype. Insome embodiments, the T cell is a Th17 T cell, and wherein the agent isadministered in an amount that is sufficient to modulate the phenotypeof the Th17 T cell to become and/or produce a CD4+ T cell phenotypeother than a Th17 T cell phenotype. In some embodiments, the T cell is aTh17 T cell, and wherein the agent is administered in an amount that issufficient to modulate the phenotype of the Th17 T cell to become and/orproduce a shift in the Th17 T cell phenotype, e.g., between pathogenicor non-pathogenic Th17 cell phenotype.

In some embodiments, the invention provides a method of inhibiting Th17differentiation in a cell population and/or increasing expression,activity and/or function of one or more non-Th17-associated cytokines,one or more non-Th17-associated receptor molecules, ornon-Th17-associated transcription factor selected from FOXP3, interferongamma (IFN-γ), GATA3, STAT4 and TBX21, comprising contacting a T cellwith an agent that enhances expression, activity and/or function of SP4,ETS2, IKZF4, TSC22D3, IRF1 or combinations thereof. In some embodiments,the agent enhances expression, activity and/or function of at least oneof SP4, IKZF4, TSC22D3 or combinations thereof. In some embodiments, theagent is an antibody, a soluble polypeptide, a polypeptide agonist, apeptide agonist, a nucleic acid agonist, a nucleic acid ligand, or asmall molecule agonist. In some embodiments, the antibody is amonoclonal antibody. In some embodiments, the T cell is a naïve T cell,and wherein the agent is administered in an amount that is sufficient tomodulate the phenotype of the T cell to become and/or produce a desirednon-Th17 T cell phenotype, for example, a regulatory T cell (Treg)phenotype or another CD4+ T cell phenotype. In some embodiments, the Tcell is a partially differentiated T cell, and wherein the agent isadministered in an amount that is sufficient to modulate the phenotypeof the partially differentiated T cell to become and/or produce adesired non-Th17 T cell phenotype, for example, a regulatory T cell(Treg) phenotype or another CD4+ T cell phenotype. In some embodiments,the T cell is a Th17 T cell, and wherein the agent is administered in anamount that is sufficient to modulate the phenotype of the Th17 T cellto become and/or produce a CD4+ T cell phenotype other than a Th17 Tcell phenotype. In some embodiments, the T cell is a Th17 T cell, andwherein the agent is administered in an amount that is sufficient tomodulate the phenotype of the Th17 T cell to become and/or produce ashift in the Th17 T cell phenotype, e.g., between pathogenic ornon-pathogenic Th17 cell phenotype.

In some embodiments, the invention provides a method of enhancing Th17differentiation in a cell population increasing expression, activityand/or function of one or more Th17-associated cytokines, one or moreTh17-associated receptor molecules, or one or more Th17-associatedtranscription regulators selected from interleukin 17F (IL-17F),interleukin 17A (IL-17A), STAT3, interleukin 21 (IL-21) and RAR-relatedorphan receptor C (RORC), and/or decreasing expression, activity and/orfunction of one or more non-Th17-associated cytokines, one or moreTh17-associated receptor molecules, or one or more non-Th17-associatedtranscription regulators selected from FOXP3, interferon gamma (IFN-γ),GATA3, STAT4 and TBX21, comprising contacting a T cell with an agentthat inhibits expression, activity and/or function of SP4, ETS2, IKZF4,TSC22D3, IRF1 or combinations thereof. In some embodiments, the agentinhibits expression, activity and/or function of at least one of SP4,IKZF4, TSC22D3 or combinations thereof. In some embodiments, the agentis an antibody, a soluble polypeptide, a polypeptide antagonist, apeptide antagonist, a nucleic acid antagonist, a nucleic acid ligand, ora small molecule antagonist. In some embodiments, the antibody is amonoclonal antibody. In some embodiments, the antibody is a chimeric,humanized or fully human monoclonal antibody. In some embodiments, the Tcell is a naïve T cell, and wherein the agent is administered in anamount that is sufficient to modulate the phenotype of the T cell tobecome and/or produce a desired Th17 T cell phenotype. In someembodiments, the T cell is a partially differentiated T cell, andwherein the agent is administered in an amount that is sufficient tomodulate the phenotype of the partially differentiated T cell to becomeand/or produce a desired Th17 T cell phenotype. In some embodiments, theT cell is a CD4+ T cell other than a Th17 T cell, and wherein the agentis administered in an amount that is sufficient to modulate thephenotype of the non-Th17 T cell to become and/or produce a Th17 T cellphenotype. In some embodiments, the T cell is a Th17 T cell, and whereinthe agent is administered in an amount that is sufficient to modulatethe phenotype of the Th17 T cell to become and/or produce a shift in theTh17 T cell phenotype, e.g., between pathogenic or non-pathogenic Th17cell phenotype.

In some embodiments, the invention provides a method of enhancing Th17differentiation in a cell population, increasing expression, activityand/or function of one or more Th17-associated cytokines, one or moreTh17-associated receptor molecules, and/or one or more Th17-associatedtranscription regulators selected from interleukin 17F (IL-17F),interleukin 17A (IL-17A), STAT3, interleukin 21 (IL-21) and RAR-relatedorphan receptor C (RORC), and/or decreasing expression, activity and/orfunction of one or more non-Th17-associated cytokines, one or moreTh17-associated receptor molecules, or one or more non-Th17-associatedtranscription regulators selected from FOXP3, interferon gamma (IFN-γ),GATA3, STAT4 and TBX21, comprising contacting a T cell with an agentthat enhances expression, activity and/or function of MINA, MYC, NKFB1,NOTCH, PML, POU2AF1, PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1,EGR2, ETV6, FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3 orcombinations thereof. In some embodiments, the agent enhancesexpression, activity and/or function of at least one of MINA, PML,POU2AF1, PROCR, SMARCA4, ZEB1, EGR2, CCR6, FAS or combinations thereof.In some embodiments, the agent is an antibody, a soluble polypeptide, apolypeptide agonist, a peptide agonist, a nucleic acid agonist, anucleic acid ligand, or a small molecule agonist. In some embodiments,the antibody is a monoclonal antibody. In some embodiments, the antibodyis a chimeric, humanized or fully human monoclonal antibody. In someembodiments, the agent is administered in an amount sufficient toinhibit Foxp3, IFN-γ, GATA3, STAT4 and/or TBX21 expression, activityand/or function. In some embodiments, the T cell is a naïve T cell, andwherein the agent is administered in an amount that is sufficient tomodulate the phenotype of the T cell to become and/or produce a desiredTh17 T cell phenotype. In some embodiments, the T cell is a partiallydifferentiated T cell, and wherein the agent is administered in anamount that is sufficient to modulate the phenotype of the partiallydifferentiated T cell to become and/or produce a desired Th17 T cellphenotype. In some embodiments, the T cell is a CD4+ T cell other than aTh17 T cell, and wherein the agent is administered in an amount that issufficient to modulate the phenotype of the non-Th17 T cell to becomeand/or produce a Th17 T cell phenotype. In some embodiments, the T cellis a Th17 T cell, and wherein the agent is administered in an amountthat is sufficient to modulate the phenotype of the Th17 T cell tobecome and/or produce a shift in the Th17 T cell phenotype, e.g.,between pathogenic or non-pathogenic Th17 cell phenotype.

In some embodiments, the invention provides a method of identifyinggenes or genetic elements associated with Th17 differentiationcomprising: a) contacting a T cell with an inhibitor of Th17differentiation or an agent that enhances Th17 differentiation; and b)identifying a gene or genetic element whose expression is modulated bystep (a). In some embodiments, the method also comprises c) perturbingexpression of the gene or genetic element identified in step b) in a Tcell that has been in contact with an inhibitor of Th17 differentiationor an agent that enhances Th17 differentiation; and d) identifying agene whose expression is modulated by step c). In some embodiments, theinhibitor of Th17 differentiation is an agent that inhibits theexpression, activity and/or function of MINA, MYC, NKFB1, NOTCH, PML,POU2AF1, PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5, CCR6, EGR1, EGR2, ETV6,FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3 or combinations thereof.In some embodiments, the agent inhibits expression, activity and/orfunction of at least one of MINA, PML, POU2AF1, PROCR, SMARCA4, ZEB1,EGR2, CCR6, FAS or combinations thereof. In some embodiments, theinhibitor of Th17 differentiation is an agent that enhances expression,activity and/or function of SP4, ETS2, IKZF4, TSC22D3, IRF1 orcombinations thereof. In some embodiments, the agent enhancesexpression, activity and/or function of at least one of SP4, IKZF4 orTSC22D3. In some embodiments, the agent that enhances Th17differentiation is an agent that inhibits expression, activity and/orfunction of SP4, ETS2, IKZF4, TSC22D3, IRF1 or combinations thereof. Insome embodiments, wherein the agent that enhances Th17 differentiationis an agent that enhances expression, activity and/or function of MINA,MYC, NKFB1, NOTCH, PML, POU2AF1, PROCR, RBPJ, SMARCA4, ZEB1, BATF, CCR5,CCR6, EGR1, EGR2, ETV6, FAS, IL12RB1, IL17RA, IL21R, IRF4, IRF8, ITGA3or combinations thereof. In some embodiments, the agent is an antibody,a soluble polypeptide, a polypeptide antagonist, a peptide antagonist, anucleic acid antagonist, a nucleic acid ligand, or a small moleculeantagonist.

In some embodiments, the invention provides a method of modulatinginduction of Th17 differentiation comprising contacting a T cell with anagent that modulates expression, activity and/or function of one or moretarget genes or one or more products of one or more target genesselected from IRF1, IRF8, IRF9, STAT2, STAT3, IRF7, STAT1, ZFP281,IFI35, REL, TBX21, FLI1, BATF, IRF4, one or more of the target geneslisted in Table 5 as being associated with the early stage of Th17differentiation, maintenance and/or function, e.g., AES, AHR, ARID5A,BATF, BCL11B, BCL3, CBFB, CBX4, CHD7, CITED2, CREB1, E2F4, EGR1, EGR2,ELL2, ETS1, ETS2, ETV6, EZH1, FLI1, FOXO1, GATA3, GATAD2B, HIF1A, ID2,IFI35, IKZF4, IRF1, IRF2, IRF3, IRF4, IRF7, IRF9, JMJD1C, JUN, LEF1,LRRFIP1, MAX, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCH1, NR3C1, PHF21A,PML, PRDM1, REL, RELA, RUNX1, SAP18, SATB1, SMAD2, SMARCA4, SP100, SP4,STAT1, STAT2, STAT3, STAT4, STAT5B, STAT6, TFEB, TP53, TRIM24, and/orZFP161, or any combination thereof.

In some embodiments, the invention provides a method of modulating onsetof Th17 phenotype and amplification of Th17 T cells comprisingcontacting a T cell with an agent that modulates expression, activityand/or function of one or more target genes or one or more products ofone or more target genes selected from IRF8, STAT2, STAT3, IRF7, JUN,STAT5B, ZPF2981, CHD7, TBX21, FLI1, SATB1, RUNX1, BATF, RORC, SP4, oneor more of the target genes listed in Table 5 as being associated withthe intermediate stage of Th17 differentiation, maintenance and/orfunction, e.g., AES, AHR, ARID3A, ARID5A, ARNTL, ASXL1, BATF, BCL11B,BCL3, BCL6, CBFB, CBX4, CDC5L, CEBPB, CHD7, CREB1, CREB3L2, CREM, E2F4,E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, ETV6, EZH1, FLI1, FOSL2,FOXJ2, FOXO1, FUS, HIF1A, HMGB2, ID1, ID2, IFI35, IKZF4, IRF3, IRF4,IRF7, IRF8, IRF9, JUN, JUNB, KAT2B, KLF10, KLF6, KLF9, LEF1, LRRFIP1,MAFF, MAX, MAZ, MINA, MTA3, MYC, MYST4, NCOA1, NCOA3, NFE2L2, NFIL3,NFKB1, NMI, NOTCH1, NR3C1, PHF21A, PML, POU2AF1, POU2F2, PRDM1, RARA,RBPJ, RELA, RORA, RUNX1, SAP18, SATB1, SKI, SKIL, SMAD2, SMAD7, SMARCA4,SMOX, SP1, SP4, SS18, STAT1, STAT2, STAT3, STAT5A, STAT5B, STAT6, SUZ12,TBX21, TFEB, TLE1, TP53, TRIM24, TRIM28, TRPS1, VAV1, ZEB1, ZEB2,ZFP161, ZFP62, ZNF238, ZNF281, and/or ZNF703, or any combinationthereof.

In some embodiments, the invention provides a method of modulatingstabilization of Th17 cells and/or modulating Th17-associatedinterleukin 23 (IL-23) signaling comprising contacting a T cell with anagent that modulates expression, activity and/or function of one or moretarget genes or one or more products of one or more target genesselected from STAT2, STAT3, JUN, STAT5B, CHD7, SATB1, RUNX1, BATF, RORC,SP4 IRF4, one or more of the target genes listed in Table 5 as beingassociated with the late stage of Th17 differentiation, maintenanceand/or function, e.g., AES, AHR, ARID3A, ARID5A, ARNTL, ASXL1, ATF3,ATF4, BATF, BATF3, BCL11B, BCL3, BCL6, C21ORF66, CBFB, CBX4, CDC5L,CDYL, CEBPB, CHD7, CHMP1B, CIC, CITED2, CREB1, CREB3L2, CREM, CSDA,DDIT3, E2F1, E2F4, E2F8, EGR1, EGR2, ELK3, ELL2, ETS1, ETS2, EZH1, FLI1,FOSL2, FOXJ2, FOXO1, FUS, GATA3, GATAD2B, HCLS1, HIF1A, ID1, ID2, IFI35,IKZF4, IRF3, IRF4, IRF7, IRF8, IRF9, JARID2, JMJD1C, JUN, JUNB, KAT2B,KLF10, KLF6, KLF7, KLF9, LASS4, LEF1, LRRFIP1, MAFF, MAX, MEN1, MINA,MTA3, MXI1, MYC, MYST4, NCOA1, NCOA3, NFE2L2, NFIL3, NFKB1, NMI, NOTCH1,NR3C1, PHF13, PHF21A, PML, POU2AF1, POU2F2, PRDM1, RARA, RBPJ, REL,RELA, RNF11, RORA, RORC, RUNX1, RUNX2, SAP18, SAP30, SATB1, SERTAD1,SIRT2, SKI, SKIL, SMAD2, SMAD4, SMAD7, SMARCA4, SMOX, SP1, SP100, SP4,SS18, STAT1, STAT3, STAT4, STAT5A, STAT5B, STATE, SUZ12, TBX21, TFEB,TGIF1, TLE1, TP53, TRIM24, TRPS1, TSC22D3, UBE2B, VAV1, VAX2, XBP1,ZEB1, ZEB2, ZFP161, ZFP36L1, ZFP36L2, ZNF238, ZNF281, ZNF703, ZNRF1,and/or ZNRF2, or any combination thereof.

In some embodiments, the invention provides a method of modulating oneor more of the target genes listed in Table 6 as being associated withthe early stage of Th17 differentiation, maintenance and/or function,e.g., FAS, CCR5, IL6ST, IL17RA, IL2RA, MYD88, CXCR5, PVR, IL15RA,IL12RB1, or any combination thereof.

In some embodiments, the invention provides a method of modulating oneor more of the target genes listed in Table 6 as being associated withthe intermediate stage of Th17 differentiation, maintenance and/orfunction, e.g., IL7R, ITGA3, IL1R1, CCR5, CCR6, ACVR2A, IL6ST, IL17RA,CCR8, DDR1, PROCR, IL2RA, IL12RB2, MYD88, PTPRJ, TNFRSF13B, CXCR3,IL1RN, CXCR5, CCR4, IL4R, IL2RB, TNFRSF12A, CXCR4, KLRD1, IRAK1BP1, PVR,IL12RB1, IL18R1, TRAF3, or any combination thereof.

In some embodiments, the invention provides a method of modulating oneor more of the target genes listed in Table 6 as being associated withthe late stage of Th17 differentiation, maintenance and/or function,e.g., IL7R, ITGA3, IL1R1, FAS, CCR5, CCR6, ACVR2A, IL6ST, IL17RA, DDR1,PROCR, IL2RA, IL12RB2, MYD88, BMPR1A, PTPRJ, TNFRSF13B, CXCR3, IL1RN,CXCR5, CCR4, IL4R, IL2RB, TNFRSF12A, CXCR4, KLRD1, IRAK1BP1, PVR,IL15RA, TLR1, ACVR1B, IL12RB1, IL18R1, TRAF3, IFNGR1, PLAUR, IL21R,IL23R, or any combination thereof.

In some embodiments, the invention provides a method of modulating oneor more of the target genes listed in Table 7 as being associated withthe early stage of Th17 differentiation, maintenance and/or function,e.g., EIF2AK2, DUSP22, HK2, RIPK1, RNASEL, TEC, MAP3K8, SGK1, PRKCQ,DUSP16, BMP2K, PIM2, or any combination thereof.

In some embodiments, the invention provides a method of modulating oneor more of the target genes listed in Table 7 as being associated withthe intermediate stage of Th17 differentiation, maintenance and/orfunction, e.g., PSTPIP1, PTPN1, ACP5, TXK, RIPK3, PTPRF, NEK4, PPME1,PHACTR2, HK2, GMFG, DAPP1, TEC, GMFB, PIM1, NEK6, ACVR2A, FES, CDK6,ZAK, DUSP14, SGK1, JAK3, ULK2, PTPRJ, SPHK1, TNK2, PCTK1, MAP4K3,TGFBR1, HK1, DDR1, BMP2K, DUSP10, ALPK2, or any combination thereof.

In some embodiments, the invention provides a method of modulating oneor more of the target genes listed in Table 7 as being associated withthe late stage of Th17 differentiation, maintenance and/or function,e.g., PTPLA, PSTPIP1, TK1, PTEN, BPGM, DCK, PTPRS, PTPN18, MKNK2, PTPN1,PTPRE, SH2D1A, PLK2, DUSP6, CDC25B, SLK, MAP3K5, BMPR1A, ACP5, TXK,RIPK3, PPP3CA, PTPRF, PACSIN1, NEK4, PIP4K2A, PPME1, SRPK2, DUSP2,PHACTR2, DCLK1, PPP2R5A, RIPK1, GK, RNASEL, GMFG, STK4, HINT3, DAPP1,TEC, GMFB, PTPN6, RIPK2, PIM1, NEK6, ACVR2A, AURKB, FES, ACVR1B, CDK6,ZAK, VRK2, MAP3K8, DUSP14, SGK1, PRKCQ, JAK3, ULK2, HIPK2, PTPRJ, INPP1,TNK2, PCTK1, DUSP1, NUDT4, TGFBR1, PTP4A1, HK1, DUSP16, ANP32A, DDR1,ITK, WNK1, NAGK, STK38, BMP2K, BUB1, AAK1, SIK1, DUSP10, PRKCA, PIM2,STK17B, TK2, STK39, ALPK2, MST4, PHLPP1, or any combination thereof.

In some embodiments, the invention provides a method of modulating isone or more of the target genes listed in Table 8 as being associatedwith the early stage of Th17 differentiation, maintenance and/orfunction, e.g., HK2, CDKN1A, DUT, DUSP1, NADK, LIMK2, DUSP11, TAOK3,PRPS1, PPP2R4, MKNK2, SGK1, BPGM, TEC, MAPK6, PTP4A2, PRPF4B, ACP1,CCRN4L, or any combination thereof.

In some embodiments, the invention provides a method of modulating oneor more of the target genes listed in Table 8 as being associated withthe intermediate stage of Th17 differentiation, maintenance and/orfunction, e.g., HK2, ZAP70, NEK6, DUSP14, SH2D1A, ITK, DUT, PPP1R11,DUSP1, PMVK, TK1, TAOK3, GMFG, PRPS1, SGK1, TXK, WNK1, DUSP19, TEC,RPS6KA1, PKM2, PRPF4B, ADRBK1, CKB, ULK2, PLK1, PPP2R5A, PLK2, or anycombination thereof.

In some embodiments, the invention provides a method of modulating oneor more of the target genes listed in Table 8 as being associated withthe late stage of Th17 differentiation, maintenance and/or function,e.g., ZAP70, PFKP, NEK6, DUSP14, SH2D1A, INPP5B, ITK, PFKL, PGK1,CDKN1A, DUT, PPP1R11, DUSP1, PMVK, PTPN22, PSPH, TK1, PGAM1, LIMK2,CLK1, DUSP11, TAOK3, RIOK2, GMFG, UCKL1, PRPS1, PPP2R4, MKNK2, DGKA,SGK1, TXK, WNK1, DUSP19, CHP, BPGM, PIP5K1A, TEC, MAP2K1, MAPK6,RPS6KA1, PTP4A2, PKM2, PRPF4B, ADRBK1, CKB, ACP1, ULK2, CCRN4L, PRKCH,PLK1, PPP2R5A, PLK2, or any combination thereof.

In some embodiments, the invention provides a method of modulating oneor more of the target genes listed in Table 9 as being associated withthe early stage of Th17 differentiation, maintenance and/or function,e.g., CD200, CD40LG, CD24, CCND2, ADAM17, BSG, ITGAL, FAS, GPR65,SIGMAR1, CAP1, PLAUR, SRPRB, TRPV2, IL2RA, KDELR2, TNFRSF9, or anycombination thereof.

In some embodiments, the invention provides a method of modulating oneor more of the target genes listed in Table 9 as being associated withthe intermediate stage of Th17 differentiation, maintenance and/orfunction, e.g., CTLA4, CD200, CD24, CD5L, CD9, IL2RB, CD53, CD74, CAST,CCR6, IL2RG, ITGAV, FAS, IL4R, PROCR, GPR65, TNFRSF18, RORA, IL1RN,RORC, CYSLTR1, PNRC2, LOC390243, ADAM10, TNFSF9, CD96, CD82, SLAMF7,CD27, PGRMC1, TRPV2, ADRBK1, TRAF6, IL2RA, THY1, IL12RB2, TNFRSF9, orany combination thereof.

In some embodiments, the invention provides a method of modulating oneor more of the target genes listed in Table 9 as being associated withthe late stage of Th17 differentiation, maintenance and/or function,e.g., CTLA4, TNFRSF4, CD44, PDCD1, CD200, CD247, CD24, CD5L, CCND2, CD9,IL2RB, CD53, CD74, ADAM17, BSG, CAST, CCR6, IL2RG, CD81, CD6, CD48,ITGAV, TFRC, ICAM2, ATP1B3, FAS, IL4R, CCR7, CD52, PROCR, GPR65,TNFRSF18, FCRL1, RORA, IL1RN, RORC, P2RX4, SSR2, PTPN22, SIGMAR1,CYSLTR1, LOC390243, ADAM10, TNFSF9, CD96, CAP1, CD82, SLAMF7, PLAUR,CD27, SIVA1, PGRMC1, SRPRB, TRPV2, NR1H2, ADRBK1, GABARAPL1, TRAF6,IL2RA, THY1, KDELR2, IL12RB2, TNFRSF9, SCARB1, IFNGR1, or anycombination thereof.

In some embodiments, the invention provides a method of inhibiting tumorgrowth in a subject in need thereof by administering to the subject atherapeutically effective amount of an inhibitor of Protein C Receptor(PROCR). In some embodiments, the inhibitor of PROCR is an antibody, asoluble polypeptide, a polypeptide agent, a peptide agent, a nucleicacid agent, a nucleic acid ligand, or a small molecule agent. In someembodiments, the inhibitor of PROCR is one or more agents selected fromthe group consisting of lipopolysaccharide; cisplatin; fibrinogen; 1,10-phenanthroline; 5-N-ethylcarboxamido adenosine; cystathionine;hirudin; phospholipid; Drotrecogin alfa; VEGF; Phosphatidylethanolamine;serine; gamma-carboxyglutamic acid; calcium; warfarin; endotoxin;curcumin; lipid; and nitric oxide.

In some embodiments, the invention provides a method of diagnosing animmune response in a subject, comprising detecting a level ofexpression, activity and/or function of one or more signature genes orone or more products of one or more signature genes selected from thoselisted in Table 1 or Table 2 and comparing the detected level to acontrol of level of signature gene or gene product expression, activityand/or function, wherein a difference between the detected level and thecontrol level indicates that the presence of an immune response in thesubject. In some embodiments, the immune response is an autoimmuneresponse. In some embodiments, the immune response is an inflammatoryresponse, including inflammatory response(s) associated with anautoimmune response and/or inflammatory response(s) associated with aninfectious disease or other pathogen-based disorder.

In some embodiments, the invention provides a method of monitoring animmune response in a subject, comprising detecting a level ofexpression, activity and/or function of one or more signature genes orone or more products of one or more signature genes, e.g., one or moresignature genes selected from those listed in Table 1 or Table 2 at afirst time point, detecting a level of expression, activity and/orfunction of one or more signature genes or one or more products of oneor more signature genes, e.g., one or more signature genes selected fromthose listed in Table 1 or Table 2 at a second time point, and comparingthe first detected level of expression, activity and/or function withthe second detected level of expression, activity and/or function,wherein a change between the first and second detected levels indicatesa change in the immune response in the subject. In some embodiments, theimmune response is an autoimmune response. In some embodiments, theimmune response is an inflammatory response.

In some embodiments, the invention provides a method of monitoring animmune response in a subject, comprising isolating a population of Tcells from the subject at a first time point, determining a first ratioof T cell subtypes within the T cell population at a first time point,isolating a population of T cells from the subject at a second timepoint, determining a second ratio of T cell subtypes within the T cellpopulation at a second time point, and comparing the first and secondratio of T cell subtypes, wherein a change in the first and seconddetected ratios indicates a change in the immune response in thesubject. In some embodiments, the immune response is an autoimmuneresponse. In some embodiments, the immune response is an inflammatoryresponse.

In some embodiments, the invention provides a method of activatingtherapeutic immunity by exploiting the blockade of immune checkpoints.The progression of a productive immune response requires that a numberof immunological checkpoints be passed. Immunity response is regulatedby the counterbalancing of stimulatory and inhibitory signal. Theimmunoglobulin superfamily occupies a central importance in thiscoordination of immune responses, and the CD28/cytotoxic T-lymphocyteantigen-4 (CTLA-4):B7.1/B7.2 receptor/ligand grouping represents thearchetypal example of these immune regulators (see e.g., Korman A J,Peggs K S, Allison J P, “Checkpoint blockade in cancer immunotherapy.”Adv Immunol. 2006; 90:297-339). In part the role of these checkpoints isto guard against the possibility of unwanted and harmful self-directedactivities. While this is a necessary function, aiding in the preventionof autoimmunity, it may act as a barrier to successful immunotherapiesaimed at targeting malignant self-cells that largely display the samearray of surface molecules as the cells from which they derive. Theexpression of immune-checkpoint proteins can be dysregulated in adisease or disorder and can be an important immune resistance mechanism.Therapies aimed at overcoming these mechanisms of peripheral tolerance,in particular by blocking the inhibitory checkpoints, offer thepotential to generate therapeutic activity, either as monotherapies orin synergism with other therapies.

Thus, the present invention relates to a method of engineering T-cells,especially for immunotherapy, comprising modulating T cell balance toinactivate or otherwise inhibit at least one gene or gene productinvolved in the immune check-point.

Suitable T cell modulating agent(s) for use in any of the compositionsand methods provided herein include an antibody, a soluble polypeptide,a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleicacid ligand, or a small molecule agent. By way of non-limiting example,suitable T cell modulating agents or agents for use in combination withone or more T cell modulating agents are shown in Table 10 of thespecification.

One skilled in the art will appreciate that the T cell modulating agentshave a variety of uses. For example, the T cell modulating agents areused as therapeutic agents as described herein. The T cell modulatingagents can be used as reagents in screening assays, diagnostic kits oras diagnostic tools, or these T cell modulating agents can be used incompetition assays to generate therapeutic reagents.

In one embodiment, the invention relates to a method of diagnosing,prognosing and/or staging an immune response involving T cell balance,which may comprise detecting a first level of expression, activityand/or function of one or more signature genes or one or more productsof one or more signature genes selected from the genes of Table 1 orTable 2 and comparing the detected level to a control of level ofsignature gene or gene product expression, activity and/or function,wherein a difference in the detected level and the control levelindicates that the presence of an immune response in the subject.

In another embodiment, the invention relates to a method of monitoringan immune response in a subject comprising detecting a level ofexpression, activity and/or function of one or more signature genes orone or more products of one or more signature genes of Table 1 or Table2 at a first time point, detecting a level of expression, activityand/or function of one or more signature genes or one or more productsof one or more signature genes of Table 1 or Table 2 at a second timepoint, and comparing the first detected level of expression, activityand/or function with the second detected level of expression, activityand/or function, wherein a change in the first and second detectedlevels indicates a change in the immune response in the subject.

In yet another embodiment, the invention relates to a method ofidentifying a patient population at risk or suffering from an immuneresponse which may comprise detecting a level of expression, activityand/or function of one or more signature genes or one or more productsof one or more signature genes of Table 1 or Table 2 in the patientpopulation and comparing the level of expression, activity and/orfunction of one or more signature genes or one or more products of oneor more signature genes of Table 1 or Table 2 in a patient populationnot at risk or suffering from an immune response, wherein a differencein the level of expression, activity and/or function of one or moresignature genes or one or more products of one or more signature genesof Table 1 or Table 2 in the patient populations identifies the patientpopulation as at risk or suffering from an immune response.

In still another embodiment, the invention relates to a method formonitoring subjects undergoing a treatment or therapy for an aberrantimmune response to determine whether the patient is responsive to thetreatment or therapy which may comprise detecting a level of expression,activity and/or function of one or more signature genes or one or moreproducts of one or more signature genes of Table 1 or Table 2 in theabsence of the treatment or therapy and comparing the level ofexpression, activity and/or function of one or more signature genes orone or more products of one or more signature genes of Table 1 or Table2 in the presence of the treatment or therapy, wherein a difference inthe level of expression, activity and/or function of one or moresignature genes or one or more products of one or more signature genesof Table 1 or Table 2 in the presence of the treatment or therapyindicates whether the patient is responsive to the treatment or therapy.

The invention may also involve a method of modulating T cell balance,the method which may comprise contacting a T cell or a population of Tcells with a T cell modulating agent in an amount sufficient to modifydifferentiation, maintenance and/or function of the T cell or populationof T cells by altering balance between Th17 cells, regulatory T cells(Tregs) and other T cell subsets as compared to differentiation,maintenance and/or function of the T cell or population of T cells inthe absence of the T cell modulating agent.

The immune response may be an autoimmune response or an inflammatoryresponse. The inflammatory response may be associated with an autoimmuneresponse, an infectious disease and/or a pathogen-based disorder.

The signature genes may be Th17-associated genes.

The treatment or therapy may be an antagonist for GPR65 in an amountsufficient to induce differentiation toward regulatory T cells (Tregs),Th1 cells, or a combination of Tregs and Th1 cells. The treatment ortherapy may be an agonist that enhances or increases the expression ofGPR65 in an amount sufficient to induce T cell differentiation towardTh17 cells. The treatment or therapy may be specific for a target geneselected from the group consisting of DEC1, PZLP, TCF4 and CD5L. Thetreatment or therapy may be an antagonist of a target gene selected fromthe group consisting of DEC1, PZLP, TCF4 and CD5L in an amountsufficient to switch Th17 cells from a pathogenic to non-pathogenicsignature. The treatment or therapy may be an agonist that enhances orincreases the expression of a target gene selected from the groupconsisting of DEC1, PZLP, TCF4 and CD5L in an amount sufficient toswitch Th17 cells from a non-pathogenic to a pathogenic signature.

The T cell modulating agent may be an antibody, a soluble polypeptide, apolypeptide agent, a peptide agent, a nucleic acid agent, a nucleic acidligand, or a small molecule agent.

The T cells may be naïve T cells, partially differentiated T cells,differentiated T cells, a combination of naïve T cells and partiallydifferentiated T cells, a combination of naïve T cells anddifferentiated T cells, a combination of partially differentiated Tcells and differentiated T cells, or a combination of naïve T cells,partially differentiated T cells and differentiated T cells.

The invention also involves a method of enhancing Th17 differentiationin a cell population, increasing expression, activity and/or function ofone or more Th17-associated cytokines or one or more Th17-associatedtranscription regulators selected from interleukin 17F (IL-17F),interleukin 17A (IL-17A), STAT3, interleukin 21 (IL-21) and RAR-relatedorphan receptor C (RORC), and/or decreasing expression, activity and/orfunction of one or more non-Th17-associated cytokines ornon-Th17-associated transcription regulators selected from FOXP3,interferon gamma (IFN-γ), GATA3, STAT4 and TBX21, comprising contactinga T cell with an agent that enhances expression, activity and/orfunction of CD5L, DEC1, PLZP, TCF4 or combinations thereof. The agentmay enhance expression, activity and/or function of at least one ofCD5L, DEC1, PLZP, or TCF4. Thw agent may be an antibody, a solublepolypeptide, a polypeptide agonist, a peptide agonist, a nucleic acidagonist, a nucleic acid ligand, or a small molecule agonist. Theantibody may be a monoclonal antibody or a chimeric, humanized or fullyhuman monoclonal antibody.

The present invention also involves the use of an antagonist for GPR65in an amount sufficient to induce differentiation toward regulatory Tcells (Tregs), Th1 cells, or a combination of Tregs and Th1 cells, useof an agonist that enhances or increases the expression of GPR65 in anamount sufficient to induce T cell differentiation toward Th17 cells,use of an antagonist of a target gene selected from the group consistingof DEC1, PZLP, TCF4 and CD5L in an amount sufficient to switch Th17cells from a pathogenic to non-pathogenic signature, use of an agonistthat enhances or increases the expression of a target gene selected fromthe group consisting of DEC1, PZLP, TCF4 and CD5L in an amountsufficient to switch Th17 cells from a non-pathogenic to a pathogenicsignature and Use of T cell modulating agent for treating an aberrantimmune response in a patient.

Accordingly, it is an object of the invention to not encompass withinthe invention any previously known product, process of making theproduct, or method of using the product such that Applicants reserve theright and hereby disclose a disclaimer of any previously known product,process, or method. It is further noted that the invention does notintend to encompass within the scope of the invention any product,process, or making of the product or method of using the product, whichdoes not meet the written description and enablement requirements of theUSPTO (35 U.S.C. § 112, first paragraph) or the EPO (Article 83 of theEPC), such that Applicants reserve the right and hereby disclose adisclaimer of any such subject matter.

It is noted that in this disclosure and particularly in the claimsand/or paragraphs, terms such as “comprises”, “comprised”, “comprising”and the like can have the meaning attributed to it in U.S. Patent law;e.g., they can mean “includes”, “included”, “including”, and the like;and that terms such as “consisting essentially of” and “consistsessentially of” have the meaning ascribed to them in U.S. Patent law,e.g., they allow for elements not explicitly recited, but excludeelements that are found in the prior art or that affect a basic or novelcharacteristic of the invention. Nothing herein is to be construed as apromise.

These and other embodiments are disclosed or are obvious from andencompassed by, the following Detailed Description.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIGS. 1A-1E are a series of graphs and illustrations depicting genomewide temporal expression profiles of Th17 differentiation. FIG. 1Adepicts an overview of approach. FIGS. 1B-1 and 1B-2 depict geneexpression profiles during Th17 differentiation. Shown are thedifferential expression levels for genes (rows) at 18 time points(columns) in Th17 polarizing conditions (TGF-β1 and IL-6; left panel,Z-normalized per row) or Th17 polarizing conditions relative to controlactivated Th0 cells (right panel, log 2(ratio)). The genes arepartitioned into 20 clusters (C1-C20, color bars, right). Right: meanexpression (Y axis) and standard deviation (error bar) at each timepoint (X axis) for genes in representative clusters. Cluster size (“n”),enriched functional annotations (“F”), and representative genes (“M”)are denoted. FIG. 1C depicts three major transcriptional phases. Shownis a correlation matrix (red (right side of correlation scale): high;blue (left side of correlation scale): low) between every pair of timepoints. FIG. 1D depicts transcriptional profiles of key cytokines andreceptor molecules. Shown are the differential expression levels (log2(ratio)) for each gene (column) at each of 18 time points (rows) inTh17 polarizing conditions (TGF-β1 and IL-6; left panel, Z-normalizedper row) vs. control activated Th0 cells.

FIGS. 2A-2G are a series of graphs and illustrations depicting a modelof the dynamic regulatory network of Th17 differentiation. FIG. 2Adepicts an overview of computational analysis. FIG. 2B depicts aschematic of temporal network ‘snapshots’. Shown are three consecutivecartoon networks (top and matrix columns), with three possibleinteractions from regulator (A) to targets (B, C & D), shown as edges(top) and matrix rows (A→B—top row; A→C—middle row; A→D—bottom row).FIG. 2C depicts 18 network ‘snapshots’. Left: each row corresponds to aTF-target interaction that occurs in at least one network; columnscorrespond to the network at each time point. A purple entry:interaction is active in that network. The networks are clustered bysimilarity of active interactions (dendrogram, top), forming threetemporally consecutive clusters (early, intermediate, late, bottom).Right: a heatmap denoting edges for selected regulators. FIG. 2D depictsdynamic regulator activity. Shown is, for each regulator (rows), thenumber of target genes (normalized by its maximum number of targets) ineach of the 18 networks (columns, left), and in each of the threecanonical networks (middle) obtained by collapsing (arrows). Right:regulators chosen for perturbation (pink), known Th17 regulators (grey),and the maximal number of target genes across the three canonicalnetworks (green, ranging from 0 to 250 targets). FIGS. 2E-1, 2E-2, and2E-3 depict that at the heart of each network is its ‘transcriptionalcircuit’, connecting active TFs to target genes that themselves encodeTFs. The transcription factor circuits shown (in each of the 3 canonicalnetworks) are the portions of each of the inferred networks associatingtranscription regulators to targets that themselves encode transcriptionregulators. Yellow nodes denote transcription factor genes that areover-expressed (compared to Th0) during the respective time segment.Edge color reflects the data type supporting the regulatory interaction(legend).

FIGS. 3A-3D are a series of graphs and illustrations depicting knockdownscreen in Th17 differentiation using silicon nanowires. FIG. 3A depictsunbiased ranking of perturbation candidates. Shown are the genes orderedfrom left to right based on their ranking for perturbation (columns, topranking is leftmost). Two top matrices: criteria for ranking by ‘NetworkInformation’ (topmost) and ‘Gene Expression Information’. Purple entry:gene has the feature (intensity proportional to feature strength; topfive features are binary). Bar chart: ranking score. ‘Perturbed’ row:dark grey: genes successfully perturbed by knockdown followed by highquality mRNA quantification; light grey: genes where an attempt toknockdown was made, but could not achieve or maintain sufficientknockdown or did not obtain enough replicates; Black: genes perturbed byknockout or for which knockout data was already available. Known row:orange entry: a gene was previously associated with Th17 function (thisinformation was not used to rank the genes; FIG. 10A, 10B). FIG. 3Bdepicts scanning electron micrograph of primary T cells (false coloredpurple) cultured on vertical silicon nanowires. FIG. 3C depicts deliveryby silicon nanowire neither activates nor induces differentiation ofnaïve T cells and does not affect their response to conventional TCRstimulation with anti-CD3/CD28. FIG. 3D depicts effective knockdown bysiRNA delivered on nanowires. Shown is the % of mRNA remaining afterknockdown (by qPCR, Y axis: mean±standard error relative tonon-targeting siRNA control, n=12, black bar on left) at 48 hrs afterintroduction of polarizing cytokines. In FIG. 3A and FIG. 3D, thecandidate regulators shown are those listed in Table 5. In FIG. 3A, thecandidate regulators are listed on the x axis and are, in order fromleft to right, RORC, SATB1, TRPS1, SMOX, RORA, ARID5A, ETV6, ARNTL,ETS1, UBE2B, BATF, STAT3, STAT1, STAT5A, NR3C1, STATE, TRIM24, HIF1A,IRF4, IRF8, ETS2, JUN, RUNX1, FLI1, REL, SP4, EGR2, NFKB1, ZFP281,STAT4, RELA, TBX21, STAT5B, IRF7, STAT2, IRF3, XBP1, FOXO1, PRDM1, ATF4,IRF1, GATA3, EGR1, MYC, CREB1, IRF9, IRF2, FOXJ2, SMARCA4, TRP53, SUZ12,POU2AF1, CEBPB, ID2, CREM, MYST4, MXI1, RBPJ, CHD7, CREB3L2, VAX2,KLF10, SKI, ELK3, ZEB1, PML, SERTAD1, NOTCH1, LRRFIP1, AHR,1810007M14RIK, SAP30, ID1, ZFP238, VAV1, MINA, BATF3, CDYL, IKZF4,NCOA1, BCL3, JUNB, SS18, PHF13, MTA3, ASXL1, LASS4, SKIL, DDIT3, FOSL2,RUNX2, TLE1, ATF3, ELL2, AES, BCL11B, JARID2, KLF9, KAT2B, KLF6, E2F8,BCL6, ZNRF2, TSC22D3, KLF7, HMGB2, FUS, SIRT2, MAFF, CHMP1B, GATAD2B,SMAD7, ZFP703, ZNRF1, JMJD1C, ZFP36L2, TSC22D4, NFE2L2, RNF11, ARID3A,MEN1, RARA, CBX4, ZFP62, CIC, HCLS1, ZFP36L1, TGIF1.

FIGS. 4A-4D are a series of graphs and illustrations depicting coupledand mutually-antagonistic modules in the Th17 network. A color versionof these figures can be found in Yosef et al., “Dynamic regulatorynetwork controlling Th17 cell differentiation, Nature, vol. 496: 461-468(2013)/doi: 10.1038/nature11981. FIG. 4A depicts the impact of perturbedgenes on a 275-gene signature. Shown are changes in the expression of275 signature genes (rows) following knockdown or knockout (KO) of 39factors (columns) at 48 hr (as well as IL-21r and IL-17ra KO at 60hours). Blue (left side of Fold change (log 2) scale): decreasedexpression of target following perturbation of a regulator (compared toa non-targeting control); red (right side of Fold change (log 2) scale):increased expression; Grey: not significant; all color (i.e., non-grey)entries are significant (see Methods in Example 1). ‘Perturbed’ (left):signature genes that are also perturbed as regulators (black entries).Key signature genes are denoted on right. FIG. 4B depicts two coupledand opposing modules. Shown is the perturbation network associating the‘positive regulators’ (blue nodes, left side of x-axis) of Th17signature genes, the ‘negative regulators’ (red nodes, right side ofx-axis), Th17 signature genes (grey nodes, bottom) and signature genesof other CD4+ T cells (grey nodes, top). A blue edge from node A to Bindicates that knockdown of A downregulates B; a red edge indicates thatknockdown of A upregulates B. Light grey halos: regulators notpreviously associated with Th17 differentiation. FIG. 4C depicts howknockdown effects validate edges in network model. Venn diagram: comparethe set of targets for a factor in the original model of FIG. 2a (pinkcircle) to the set of genes that respond to that factor's knockdown inan RNA-Seq experiment (yellow circle). Bar chart on bottom: Shown is the−log 10(Pvalue) (Y axis, hypergeometric test) for the significance ofthis overlap for four factors (X axis). Similar results were obtainedwith a non-parametric rank-sum test (Mann-Whitney U test, see Methods inExample 1). Red dashed line: P=0.01. FIG. 4D depicts how globalknockdown effects are consistent across clusters. Venn diagram: comparethe set of genes that respond to a factor's knockdown in an RNA-Seqexperiment (yellow circle) to each of the 20 clusters of FIG. 1b (purplecircle). The knockdown of a ‘Th17 positive’ regulator was expected torepress genes in induced clusters, and induce genes in repressedclusters (and vice versa for ‘Th17 negative’ regulators). Heat map: Foreach regulator knockdown (rows) and each cluster (columns) shown are thesignificant overlaps (non grey entries) by the test above. Red (rightside of Fold enrichment scale): fold enrichment for up-regulation uponknockdown; Blue (left side of Fold enrichment scale): fold enrichmentfor down regulation upon knockdown. Orange entries in the top rowindicate induced clusters.

FIGS. 5A-5D are a series of graphs and illustrations depicting thatMina, Fas, Pou2af1, and Tsc22d3 are key novel regulators affecting theTh17 differentiation programs. A color version of these figures can befound in Yosef et al., “Dynamic regulatory network controlling Th17 celldifferentiation, Nature, vol. 496: 461-468 (2013)/doi:10.1038/nature11981. FIGS. 5A-5D, left: Shown are regulatory networkmodels centered on different pivotal regulators (square nodes): (FIG.5A) Mina, (FIG. 5B) Fas, (FIG. 5C) Pou2af1, and (FIG. 5D) Tsc22d3. Ineach network, shown are the targets and regulators (round nodes)connected to the pivotal nodes based on perturbation (red and bluedashed edges), TF binding (black solid edges), or both (red and bluesolid edges). Genes affected by perturbing the pivotal nodes are colored(blue: target is down-regulated by knockdown of pivotal node; red:target is up-regulated). (FIGS. 5A-5C, middle and right) Intracellularstaining and cytokine assays by ELISA or Cytometric Bead Assays (CBA) onculture supernatants at 72 h of in vitro differentiated cells fromrespective KO mice activated in vitro with anti-CD3+anti-CD28 with orwithout Th17 polarizing cytokines (TGF-β+IL-6). (FIG. 5D, middle)ChIP-Seq of Tsc22d3. Shown is the proportion of overlap in bound genes(dark grey) or bound regions (light grey) between Tsc22d3 and a host ofTh17 canonical factors (X axis). All results are statisticallysignificant (P<10⁻⁶; see Methods in Example 1).

FIGS. 6A-6D are a series of graphs and illustrations depicting treatmentof Naïve CD4+ T-cells with TGF-β1 and IL-6 for three days induces thedifferentiation of Th17 cells. A color version of these figures can befound in Yosef et al., “Dynamic regulatory network controlling Th17 celldifferentiation, Nature, vol. 496: 461-468 (2013)/doi:10.1038/nature11981. FIG. 6A depicts an overview of the time courseexperiments. Naïve T cells were isolated from WT mice, and treated withIL-6 and TGF-β1. Microarrays were then used to measure global mRNAlevels at 18 different time points (0.5 hr-72 hr, see Methods in Example1). As a control, the same WT naïve T cells under Th0 conditionsharvested at the same 18 time points were used. For the last four timepoints (48 hr-72 hr), cells treated with IL-6, TGF-β1, and IL-23 werealso profiled. FIG. 6B depicts generation of Th17 cells by IL-6 andTGF-β1 polarizing conditions. FACS analysis of naïve T cellsdifferentiated with TGF-β1 and IL-6 (right) shows enrichment for IL-17producing Th17 T cells; these cells are not observed in the Th0controls. FIG. 6C depicts comparison of the obtained microarray profilesto published data from naïve T-cells and differentiated Th17 cells (Weiet. al, 2009; Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. inGenome Biol Vol. 10 R25 (2009)). Shown is the Pearson correlationcoefficient (Y axis) between each of the 18 profiles (ordered by timepoint, X axis) and either the naïve T cell profiles (blue) or thedifferentiated Th17 profiles (green). The expression profiles graduallytransition from a naïve-like state (at t=0.5 hr, r2>0.8, p<10⁻¹⁰) to aTh17 differentiated state (at t=72 hr, r2>0.65, p<10⁻¹⁰). FIG. 6Ddepicts expression of key cytokines. Shown are the mRNA levels (Y axis)as measured at each of the 18 time points (X axis) in the Th17polarizing (blue) and Th0 control (red) conditions for the key Th17genes RORc (left) and IL-17a (middle), both induced, and for thecytokine IFN-γ, unchanged in the time course.

FIG. 7 is a series of graphs depicting clusters of differentiallyexpressed genes in the Th17 time course data. A color version of thesefigures can be found in Yosef et al., “Dynamic regulatory networkcontrolling Th17 cell differentiation, Nature, vol. 496: 461-468(2013)/doi: 10.1038/nature11981. For each of the 20 clusters in FIG. 1bshown are the average expression levels (Y axis, ±standard deviation,error bars) at each time point (X axis) under Th17 polarizing (blue) andTh0 (red) conditions. The cluster size (“n”), enriched functionalannotations (“F”), and representative member genes (“M”) are denoted ontop.

FIGS. 8A-8B are a series of graphs depicting transcriptional effects ofIL-23. FIG. 8A depicts transcriptional profiles of key genes. A colorversion of these figures can be found in Yosef et al., “Dynamicregulatory network controlling Th17 cell differentiation, Nature, vol.496: 461-468 (2013)/doi: 10.1038/nature11981. Shown are the expressionlevels (Y axis) of three key genes (IL-22, RORc, IL-4) at each timepoint (X axis) in Th17 polarizing conditions (blue), Th0 controls (red),and following the addition of IL-23 (beginning at 48 hr postdifferentiation) to the Th17 polarizing conditions (green). FIG. 8Bdepicts IL-23-dependent transcriptional clusters. Shown are clusters ofdifferentially expressed genes in the IL-23r^(−/−) time course data(blue) compared to WT cells, both treated with Th17 polarizing cytokinesand IL23 (red). For each cluster, shown are the average expressionlevels (Y axis, ±standard deviation, error bars) at each time point (Xaxis) in the knockout (blue) and wildtype (red) cells. The cluster size(“n”), enriched functional annotations (“F”), and representative membergenes (“M”) are denoted on top.

FIGS. 9A-9B are a series of graphs depicting predicted and validatedprotein levels of ROR-γt during Th17 differentiation. A color version ofthese figures can be found in Yosef et al., “Dynamic regulatory networkcontrolling Th17 cell differentiation, Nature, vol. 496: 461-468(2013)/doi: 10.1038/nature11981. FIG. 9A shows RORγt mRNA levels alongthe original time course under Th17 polarizing conditions, as measuredwith microarrays (blue). A sigmoidal fit for the mRNA levels (green) isused as an input for a model (based on Schwanhäusser, B. et al. Globalquantification of mammalian gene expression control. Nature 473,337-342, doi:10.1038/nature10098 (2011)) that predicts the level ofRORγt protein at each time point (red). FIG. 9B depicts distribution ofmeasured ROR-γt protein levels (x axis) as determined by FACS analysisin Th17 polarizing conditions (blue) and Th0 conditions (red) at 4, 12,24, and 48 hr post stimulation.

FIGS. 10A-10B are a series of graphs depicting predictive features forranking candidates for knockdown. Shown is the fold enrichment (Y axis,in all cases, p<10⁻³, hypergeometric test) in a curated list of knownTh17 factors for different (FIG. 10A) network-based features and (FIG.10B) expression-base features (as used in FIG. 3A).

FIGS. 11A-11C are a series of graphs depicting Nanowire activation onT-cells, knockdown at 10 h, and consistency of NW-based knockdowns andresulting phenotypes. FIG. 11A depicts how Nanowires do not activate Tcells and do not interfere with physiological stimuli. Shown are thelevels of mRNA (mean±standard error, n=3) for keygenes, measured 48 hrafter activation by qPCR (Y axis, mean and standard error of the mean),in T cells grown in petri dishes (left) or on silicon nanowires (right)without polarizing cytokines (‘no cytokines’) or in the presence of Th17polarizing cytokines (‘TGF-β1+IL6’). FIG. 11B depicts effectiveknockdown by siRNA delivered on nanowires. Shown is the % of mRNAremaining after knockdown (by qPCR, Y axis: mean±standard error relativeto non-targeting siRNA control, n=12, black bar on left) at 10 hoursafter introduction of polarizing cytokines. The genes presented are asuperset of the 39 genes selected for transcriptional profiling. FIG.11C. Consistency of NW-based knockdowns and resulting phenotypes. Shownare average target transcript reductions and phenotypic changes (asmeasured by IL-17f and IL-17a expression) for three differentexperiments of NW-based knockdown (from at least 2 different cultures)of 9 genes at 48 hours post stimulation. Light blue bars: knockdownlevel (% remaining relative to siRNA controls); dark grey and lightgreen bars: mRNAs of IL-17f and IL-17a, respectively, relative to siRNAcontrols.

FIGS. 12A-12B are a series of graphs depicting cross-validation of theNanostring expression profiles for each nanowire-delivered knockdownusing Fluidigm 96×96 gene expression chips. FIG. 12A depicts acomparison of expression levels measured by Fluidigm (Y axis) andNanostring (X axis) for the same gene under the same perturbation.Expression values were normalized to control genes as described inExample 1. FIG. 12B depicts how analysis of Fluidigm data recapitulatesthe partitioning of targeted factors into two modules of positive andnegative Th17 regulators. Shown are the changes in transcription of the82 genes out of the 85 gene signature (rows) that significantlyresponded to at least one factor knockdown (columns).

FIG. 13 is a graph depicting rewiring of the Th17 “functional” networkbetween 10 hr to 48 hr post stimulation. For each regulator that wasprofiled at 10 hr and 48 hr, the percentage of “edges” (i.e., gene A isaffected by perturbation of gene B) that either appear in the two timepoints with the same activation/repression logic (Sustained); appearonly in one time point (Transient); or appear in both networks but witha different activation/repression logic (Flipped) were calculated. Inthe sustained edges, the perturbation effect (fold change) has to besignificant in at least one of the time point (see Methods in Example1), and consistent (in terms of activation/repression) in the other timepoint (using a more permissive cutoff of 1.25 fold).

FIG. 14 is an illustration depicting “chromatic” network motifs. A colorversion of these figures can be found in Yosef et al., “Dynamicregulatory network controlling Th17 cell differentiation, Nature, vol.496: 461-468 (2013)/doi: 10.1038/nature11981. A ‘chromatic’ networkmotif analysis was used to find recurring sub networks with the sametopology and the same node and edge colors. Shown are the foursignificantly enriched motifs (p<0.05). Red nodes: positive regulators;blue nodes: negative regulator; red edges from A to B: knockdown of Adownregulates B; blue edge: knockdown of A upregulates B. Motifs werefound using the FANMOD software (Wernicke, S. & Rasche, F. FANMOD: atool for fast network motif detection. Bioinformatics 22, 1152-1153,doi:10.1093/bioinformatics/bt1038 (2006)).

FIGS. 15A-15C are a series of graphs depicting RNA-seq analysis ofnanowire-delivered knockdowns. A color version of these figures can befound in Yosef et al., “Dynamic regulatory network controlling Th17 celldifferentiation, Nature, vol. 496: 461-468 (2013)/doi:10.1038/nature11981. FIG. 15A depicts a correlation matrix of knockdownprofiles. Shown is the Spearman rank correlation coefficient between theRNA-Seq profiles (fold change relative to NT siRNA controls) ofregulators perturbed by knockdowns. Genes that were not significantlydifferentially expressed in any of the samples were excluded from theprofiles. FIG. 15B depicts knockdown effects on known marker genes ofdifferent CD4+ T cell lineages. Shown are the expression levels forcanonical genes (rows) of different T cell lineages (labeled on right)following knockdown of each of 12 regulators (columns). Red/Blue:increase/decrease in gene expression in knockdown compared tonon-targeting control (see Methods in Example 1). Shown are only genesthat are significantly differentially expressed in at least oneknockdown condition. The experiments are hierarchically clustered,forming distinct clusters for Th17-positive regulators (left) andTh17-negative regulators (right). FIG. 15C depicts knockdown effects ontwo subclusters of the T-regulatory cell signature, as defined by Hillet al., Foxp3 transcription-factor-dependent and -independent regulationof the regulatory T cell transcriptional signature. Immunity 27,786-800, doi:S1074-7613(07)00492-X [pii] 10.1016/j.immuni.2007.09.010(2007). Each cluster (annotated in Hill et al as Clusters 1 and 5)includes genes that are over expressed in Tregs cells compared toconventional T cells. However, genes in Cluster 1 are more correlated toFoxp3 and responsive to Foxp3 transduction. Conversely, genes in cluster1 are more directly responsive to TCR and IL-2 and less responsive toFoxp3 in Treg cells. Knockdown of Th17-positive regulators stronglyinduces the expression of genes in the ‘Foxp3’ Cluster 1. The knockdownprofiles are hierarchically clustered, forming distinct clusters forTh17-positive regulators (left) and Th17-negative regulators (right).Red/Blue: increase/decrease in gene expression in knockdown compared tonon-targeting control (see Methods in Example 1). Shown are only genesthat are significantly differentially expressed in at least oneknockdown condition.

FIGS. 16A-16D are a series of graphs depicting quantification ofcytokine production in knockout cells at 72 h of in-vitrodifferentiation using Flow cytometry and Enzyme-linked immunosorbentassay (ELISA). All flow cytometry figures shown, except for Oct1, arerepresentative of at least 3 repeats, and all ELISA data has at least 3replicates. For Oct1, only a limited amount of cells were available fromreconstituted mice, allowing for only 2 repeats of the Oct1 deficientmouse for flow cytometry and ELISA. (FIG. 16A, left) Mina^(−/−) T cellsactivated under Th0 controls are controls for the graphs shown in FIG.5A. (FIG. 16A, right) TNF secretion by Mina^(−/−) and WT cells, asmeasured by cytometric bead assay showing that Mina^(−/−) T cellsproduce more TNF when compared to control. FIG. 16B depictsintracellular cytokine staining of Pou2af1^(−/−) and WT cells for IFN-γand IL-17a as measured by flow cytometry. (FIG. 16C, left) Flowcytometric analysis of Fas^(−/−) and WT cells for Foxp3 and Il-17expression. (FIG. 16C, right) IL-2 and Tnf secretion by Fas^(−/−) and WTcells, as measured by a cytokine bead assay ELISA. (FIG. 16D, left).Flow cytometry on Oct1^(−/−) and WT cells for IFN-γ and IL-17a, showingan increase in IFN-γ positive cells in the Th0 condition for the Oct1deficient mouse. (FIG. 16D, right) Il-17a, IFN-γ, IL-2 and TNFproduction by Oct1^(−/−) and WT cells, as measured by cytokine ELISA andcytometric bead assay. Statistical significance in the ELISA figures isdenoted by: *p<0.05, **p<0.01, and ***p<0.001.

FIGS. 17A-17B are a series of illustrations depicting that Zeb1,Smarca4, and Sp4 are key novel regulators affecting the Th17differentiation programs. A color version of these figures can be foundin Yosef et al., “Dynamic regulatory network controlling Th17 celldifferentiation, Nature, vol. 496: 461-468 (2013)/doi:10.1038/nature11981. Shown are regulatory network models centered ondifferent pivotal regulators (square nodes): (FIG. 17A) Zeb1 andSmarca4, and (FIG. 17B) Sp4. In each network, shown are the targets andregulators (round nodes) connected to the pivotal nodes based onperturbation (red and blue dashed edges), TF binding (black solidedges), or both (red and blue solid edges). Genes affected by perturbingthe pivotal nodes are colored (red: target is up-regulated by knockdownof pivotal node; blue: target is down-regulated).

FIG. 18 is a graph depicting the overlap with ChIP-seq and RNA-seq datafrom Ciofani et al (Cell, 2012). Fold enrichment is shown for the fourTF that were studied by Ciofani et al using ChIP-seq and RNA-seq and arepredicted as regulators in the three network models (early, intermediate(denoted as “mid”), and late). The results are compared to the ChIP-seqbased network of Ciofani et al. (blue) and to their combinedChIP-seq/RNA-seq network (taking a score cutoff of 1.5, as described bythe authors; red). In all cases the p-value of the overlap (withChIP-seq only or with the combined ChIP-seq/RNA-seq network) is below10⁻¹⁰ (using Fisher exact test), but the fold enrichment is particularlyhigh in genes that are both bound by a factor and affected by itsknockout, the most functional edges.

FIGS. 19A-19D are a series of graphs depicting that PROCR isspecifically induced in Th17 cells induced by TGF-β1 with IL-6. FIG. 19Adepicts how PROCR expression level was assessed by the microarrayanalysis under Th0 and Th17 conditions at 18 different time points. FIG.19B depicts how kinetic expression of PROCR mRNA was measured byquantitative RT-PCR analysis in Th17 cells differentiated with TGF-β1and IL-6. FIG. 19C depicts how PROCR mRNA expression was measured byquantitative RT-PCR analysis in different T cell subsets 72 hr afterstimulation by each cytokine. FIG. 19D depicts how PROCR proteinexpression was examined by flow cytometry in different T cell subsets 72hr after stimulation with each cytokine.

FIGS. 20A-20D are a series of graphs depicting that PROCR stimulationand expression is not essential for cytokine production from Th17 cells.FIG. 20A depicts how naïve CD4+ T cells were differentiated into Th17cells by anti-CD3/anti-CD28 stimulation in the presence of activatedprotein C (aPC, 300 nM), the ligand of PROCR. On day 3, cells werestimulated with PMA and Ionomycin for 4 hr, stained intracellularly forIFN-γ and IL-17 and analyzed by flow cytometry. FIG. 20B depicts IL-17production from Th17 cells (TGF-β+IL-6) differentiated with or withoutactivated protein C (aPC and Ctl, respectively) was assessed by ELISA onDay 3 and 5. FIG. 20C depicts how naïve CD4+ T cells were polarizedunder Th17 conditions (TGF-β+IL-6), transduced with either GFP controlretrovirus (Ctl RV) or PROCR-expressing retrovirus (PROCR RV).Intracellular expression of IFN-γ and IL-17 in GFP+ cells were assessedby flow cytometry. FIG. 20D depicts how naïve CD4+ T cells from EPCR δ/δmice and control mice were polarized under Th17 conditions with TGF-β1and IL-6. Intracellular expression of IFN-γ and IL-17 were assessed byflow cytometry.

FIGS. 21A-21B are a series of graphs depicting that PROCR expressiononly induces minor changes in the expression of co-stimulatory moleculeson Th17 cells. FIG. 21A depicts how naïve CD4⁺ T cells were polarizedunder Th17 conditions (TGF-β+IL-6), transduced with either GFP controlretrovirus (Ctl GFP) or PROCR-expressing retrovirus (PROCR RV) andexpression of ICOS, CTLA-4, PD-1, Pdp and Tim-3 was analyzed by flowcytometry. FIG. 21B depicts how naïve wild type (WT) or EPCR δ/δ CD4⁺ Tcells were differentiated into Th17 cells by anti-CD3/anti-CD28stimulation in the presence of TGF-β1 and IL-6. Expression of ICOS,CTLA-4, PD-1, Pdp and Tim-3 was assessed by flow cytometry.

FIGS. 22A-22C are a series of graphs depicting that PROCR is expressedin non-pathogenic Th17 cells. FIG. 22A depicts genes for Th17 cellsdifferentiated with TGF-β3+IL-6 (pathogenic) or TGF-β1+IL-6(non-pathogenic) and comparison of their expression levels in these twosubsets. FIGS. 22B and 22C depict how naïve CD4⁺ T cells weredifferentiated with TGF-β1 and IL-6, TGF-β3 and IL-6 or IL-1β and IL-6and PROCR expression was assessed by (FIG. 22B) quantitative RT-PCRanalysis (FIG. 22C) and protein expression was determined by flowcytometry.

FIGS. 23A-23C are a series of graphs depicting that PROCR stimulation orexpression impairs some pathogenic signature genes in Th17 cells. FIG.23A depicts quantitative RT-PCR analysis of mRNA expression of severalpathogenic signature genes in Th17 cells differentiated with TGFβ1 andIL-6 in the presence of activated protein C (aPC) for 3 days in vitro.FIG. 23B depicts quantitative RT-PCR analysis of mRNA expression ofseveral pathogenic signature genes in naïve CD4⁺ T cells polarized underTh17 conditions, transduced with either GFP control retrovirus (ControlRV) or PROCR-expressing retrovirus (PROCR RV) for 3 days. FIG. 23Cdepicts quantitative RT-PCR analysis of mRNA expression of severalpathogenic signature genes in Th17 cells from EPCR δ/δ mice and controlmice differentiated with TGFβ1 and IL-6 for 3 days in vitro.

FIGS. 24A-24D are a series of graphs depicting that Rorγt induces PROCRexpression under Th17 conditions polarized with TGF-β1 and IL-6. FIG.24A depicts ChIP-Seq of Rorγt. The PROCR genomic region is depicted.FIG. 24B depicts how the binding of Rorγt to the Procr promoter in Th17cells was assessed by chromatin immunoprecipitation (ChIP). ChIP wasperformed using digested chromatin from Th17 cells and anti-Rorγtantibody. DNA was analyzed by quantitative RT-PCR analysis. FIG. 24Cdepicts how naïve CD4+ T cells from Rorγt−/− mice and control mice werepolarized under Th17 conditions with TGF-β1 and IL-6 and under Th0conditions (no cytokines) and PROCR expression was analyzed on day 3 byflow cytometry. FIG. 24D depicts how naïve CD4+ T cells polarized underTh17 conditions were transduced with either GFP control retrovirus (CtlRV) or Rorγt-expressing retrovirus (Rorγt RV) for 3 days. PROCR mRNAexpression was measured by quantitative RT-PCR analysis and PROCRprotein expression was assessed by flow cytometry.

FIGS. 25A-25C are a series of graphs depicting that IRF4 and STAT3 bindto the Procr promoter and induce PROCR expression. FIG. 25A depicts howbinding of IRF4 or STAT3 to the Procr promoter was assessed by chromatinimmunoprecipitation (ChIP)-PCR. ChIP was performed using digestedchromatin from Th17 cells and anti-IRF4 or anti-STAT3 antibody. DNA wasanalyzed by quantitative RT-PCR analysis. FIG. 25B depicts how naïveCD4+ T cells from Cd4^(Cre)STAT3^(fl/fl) mice (STAT3 KO) and controlmice (WT) were polarized under Th17 conditions with TGF-β1 with IL-6 andunder Th0 condition with no cytokines. On day 3, PROCR expression wasdetermined by quantitative PCR. FIG. 25C depicts how naïve CD4+ T cellsfrom Cd4^(Cre)IRF4^(fl/fl) mice and control mice were polarized underTh17 conditions with TGF-β1 and IL-6 and under Th0 condition with nocytokines. On day 3, PROCR expression was determined by flow cytometry.

FIGS. 26A-26D are a series of graphs and illustrations depicting thatPROCR deficiency exacerbates EAE severity. FIG. 26A depicts frequency ofCD4+ T cells expressing IL-17 and PROCR isolated from EAE mice 21d afterimmunization with MOG₃₅₋₅₅. FIG. 26B depicts how EAE was induced byadoptive transfer of MOG₃₅₋₅₅-specific 2D2 cells transduced with acontrol retrovirus (Ctl GFP) or a PROCR-expression retrovirus (PROCR_RV)and differentiated into Th17 cells. Mean clinical scores and summariesfor each group are shown. Results are representative of one of twoexperiments. FIG. 26C depicts how Rag1−/− mice were reconstituted witheither PROCR-deficient (EPCR δ/δ Rag1−/−) or WT T cells (WT→Rag1−/−) andimmunized with MOG₃₅₋₅₅ to induce EAE. The mean clinical score of eachgroup is shown. Results are representative of one of two experiments.FIG. 26D depicts a schematic representation of PROCR regulation. Rorγt,IRF4, and STAT3 induce PROCR expression. PROCR ligation by activatedprotein C induces a downregulation of the pathogenic signature genesIL-3, CXCL3, CCL4 and Pdp and reduced pathogenicity in EAE.

FIGS. 27A-27C are a series of graphs depicting that FAS promotes Th17differentiation. Naïve CD4⁺ T cells from wild type (WT) or FAS-deficient(LPR) mice were differentiated into Th17 cells by anti-CD3/anti-CD28stimulation in the presence of IL-β3, IL-6 and IL-23. On day 4, cellswere (FIG. 27A) stimulated with PMA and Ionomycin for 4 hr, stainedintracellularly for IFN-γ and IL-17 and analyzed by flow cytometry and(FIG. 27B) IL-17 production was assessed by ELISA. FIG. 27C depicts howRNA was extracted and expression of IL17a and Il23r mRNA was determinedby quantitative PCR.

FIGS. 28A-28C are a series of graphs depicting that FAS inhibits Th1differentiation. Naïve CD4⁺ T cells from wild type (WT) or FAS-deficient(LPR) mice were differentiated into Th1 cells by anti-CD3/anti-CD28stimulation in the presence of IL-12 and anti-IL-4. On day 4, cells were(FIG. 28A) stimulated with PMA and Ionomycin for 4 hr, stainedintracellularly for IFN-γ and IL-17 and analyzed by flow cytometry and(FIG. 28B) IFN-γ production was assessed by ELISA. FIG. 28C depicts howRNA was extracted and expression of Ifng mRNA was determined byquantitative PCR.

FIGS. 29A-29B are a series of graphs depicting that FAS inhibits Tregdifferentiation. Naïve CD4⁺ T cells from wild type (WT) or FAS-deficient(LPR) mice were differentiated into Tregs by anti-CD3/anti-CD28stimulation in the presence of TGF-β. On day 4, cells were (FIG. 29A)stimulated with PMA and Ionomycin for 4 hr, stained intracellularly forIL-17 and Foxp3 and analyzed by flow cytometry and (FIG. 29B) IL-10production was assessed by ELISA.

FIGS. 30A-30B are a series of graphs depicting that FAS-deficient miceare resistant to EAE. Wild type (WT) or FAS-deficient (LPR) mice wereimmunized with 100 m MOG₃₅₋₅₅ in CFA s.c. and received pertussis toxini.v. to induce EAE. FIG. 30A depicts mean clinical score ±s.e.m. of eachgroup as shown. FIG. 30B depicts how on day 14 CNS infiltratinglymphocytes were isolated, re-stimulated with PMA and Ionomycin for 4hours and stained intracellularly for IL-17, IFN-γ, and Foxp3. Cellswere analyzed by flow cytometry.

FIGS. 31A-31D are a series of graphs and illustrations depicting thatPROCR is expressed on Th17 cells. FIG. 31A depicts a schematicrepresentation of PROCR, its ligand activated protein C and thesignaling adapter PAR1. FIG. 31B depicts how naïve CD4+ T cells weredifferentiated under polarizing conditions for the indicated T helpercell lineages. Expression of PROCR was determined by quantitative PCR onday 3. FIG. 31C depicts how mice were immunized for EAE, cells wereisolated at peak of disease, and cytokine production (IL-17) and PROCRexpression were analyzed by flow cytometry. FIG. 31D depicts how naïveand memory cells were isolated from WT and PROCRd/d mice and stimulatedwith anti-CD3/CD28. Naïve cells were cultured under Th17 polarizingconditions as indicated; memory cells were cultured in the presence orabsence of IL-23. After 3 days IL-17A levels in supernatants wereanalyzed by ELISA.

FIGS. 32A-32D are a series of graphs depicting how PROCR and PD-1expression affects Th17 pathogenicity. FIG. 32A depicts signature genesof pathogenic and non-pathogenic Th17 cells. Naïve CD4+ T cells weredifferentiated into non-pathogenic (TGFβ1+IL-6) or pathogenic(TGFβ3+IL-6 or IL-β1+IL-6) Th17 cells and PROCR expression wasdetermined by quantitative PCR. FIG. 32B depicts how naïve WT orPROCRd/d CD4+ T cells were stimulated under Th17 polarizing conditions(TGFβ1+IL-6) in the presence or absence of aPC. Quantitative expressionof three pathogenic signature genes was determined on day 3. FIG. 32Cdepicts how naïve 2D2 T cells were transduced with a retrovirus encodingfor PROCR or a control (GFP), differentiated into Th17 cells in vitro,and transferred into naïve recipients. Mice were monitored for EAE. FIG.32D depicts how naive 2D2 T cells were differentiated into Th17 cells invitro with TGFβ1+IL-6+IL-23 and transferred into WT or PD-L1−/−recipients. Mice were monitored for EAE.

FIGS. 33A-33B are a series of graphs depicting that PROCR expression isenriched in exhausted T cells. FIG. 33A depicts how C57BL/6 or BalbCmice were implanted with B16 melanoma or CT26 colon cancer cellsrespectively. Tumor Infiltrating Lymphocytes were isolated 3 weeks aftertumor implantation, sorted based on PD-1 and Tim3 expression andanalyzed for PROCR expression using real time PCR. Effector memory(CD44hiCD62Llo) CD8 T cells were sorted from naïve mice. FIG. 33B)depicts how PROCR, PD-1 and Tim3 expression on antigen-specific CD8 Tcells were measured by FACS from acute (Armstrong) and chronic (Clone13) LCMV infection at different times points as indicated.

FIGS. 34A-34C are a series of graphs demonstrating the expression ofCD5L on Th17 cells.

FIGS. 35A-35C are a series of illustrations and graphs depicting howCD5L deficiency does not alter Th17 differentiation.

FIGS. 36A-36B are a series of illustrations and graphs depicting howCD5L deficiency alters Th17 memory by affecting survival or stability.

FIGS. 37A-37B are a series of graphs depicting how CD5L deficiencyresults in more severe and prolonged EAE with higher Th17 responses.

FIGS. 38A-38C are a series of illustrations and graphs depicting howloss of CD5L converts non-pathogenic Th17 cells into pathogenic effectorTh17 cells.

FIGS. 39A-39B are a series of graphs depicting how CD5L-deficient Th17cells (TGF-β+IL-6) develop a pathogenic phenotype.

FIGS. 40A-40B are a series of graphs depicting IL17A expression wasreduced in GPR65 knock out cells exposed to various T cell conditions(Th0, T16, T36, B623 and T).

FIGS. 41A-41D are a series of graphs depicting that IL17A expression inDEC1 knock out cells exposed to various T cell conditions (Th0, T16,T36, B623 and T) was unchanged in the non-pathogenic condition (T16),but was reduced in the pathogenic conditions (T36, B623).

FIGS. 42A-42B are a series of graphs depicting that IL17A expression inPLZP knock out cells exposed to various T cell conditions (Th0, T16,T36, B623 and T) was unchanged in the non-pathogenic condition (T16),but was reduced in the pathogenic conditions (T36, B623).

FIG. 43 is a graph depicting IL17A expression in TCF4 knock out cellsexposed to various T cell conditions (Th0, T16, T36, B623 and T) wasreduced in the pathogenic condition B623.

FIG. 44 is a graph depicting B16 tumor inoculation of PROCR mutant mice.7 week old wild type or PROCR mutant (EPCR delta) C57BL/6 mice wereinoculated with 5×10⁵ B16F10 melanoma cells.

FIG. 45A-4511 show Single-cell RNAseq identifies Cd5l as a novelregulator associated with Th17 cell pathogenicity and expressed only bynon-pathogenic Th17 cells. Single-cells were sorted from in-vitro Th17cells differentiated with TGFβ1+IL-6 (A,B), IL-1β+IL-6+IL-23 (C),TGFβ3+IL-6 (D) and in-vivo Th17 cells from CNS of mice at the peak ofEAE (score=3) (D). IL-17A.GFP⁺ CD4⁺ T cells were sorted in all panels inD. (A) Correlation of CD5L expression in non-pathogenic Th17 cells withthe pathogenic signature (Lee, Awasthi et al.). (B) Principal ComponentAnalysis of CD5L expression where the direction of PC1 correlates withpathogenicity. (C, D) Histogram of CD5L expression in single-cell fromconditions as indicated. CD5L expression in vitro is validated by qPCR(E, F) and flow cytometry (G). FIG. 45E, F, G shows validation of CD5Lexpression in vitro. Naïve T cells (CD4⁺CD62L⁺CD44⁻CD25⁻) were sortedand activated by plate-bound anti-CD3 and anti-CD28 antibodies in thepresence of various differentiation cytokines as indicated. CD5Lexpression was measured by qPCR at 48 h (E) and 72 h (F) andintracellularly by flow cytometry at 48 h (G); (F) At 48 h, cells werelifted from plate, washed and replated in fresh media with IL-23 or PBSand cultured for additional 24 h. FIG. 45H shows validation of CD5Lexpression in vivo. IL-17A.GFP reporter mice were immunized by MOG/CFA(s.c., d1) with pertussis toxin (i.v., d1 and d3). Mice were sacrificedat the peak of disease (score=3) and CD4⁺GFP⁺ and CD4⁺GFP⁻ cells weresorted from CNS and spleen respectively. Cd5l and Il17a expression aremeasured by qPCR. Figure shown is representative data of technicalreplicates from two independent mouse experiments. I. IL-17⁺ (GFP⁺) andIL-17⁻ (GFP⁻) CD4⁺ cells were sorted from the gut of naïve mice and thenumber of RNA transcripts measured by nanostring and normalized based onfour house-keeping genes. Figure is summary of two independentexperiments.

FIG. 46A-46H shows CD5L regulates Th17 cell effector function. (A) WTand CD5L^(−/−) mice were immunized with 40 μg MOG/CFA with pertussistoxin injection (iv) on day 1 and day 3. EAE was scored as previouslypublished (Jager, Dardalhon et al. 2009). Upper panel is pooled resultsfrom 3 independent mice experiments; Lower panel is representative FACSplot showing cytokine production from CD4 T cells isolated from CNS atday 15 post immunization after 4 hours of restimulation withPMA/ionomycin. Summary data is shown in FIG. 50B. FIG. 46B, C, D showsnaïve T cells (CD4⁺CD62L⁺CD44⁻CD25⁻) were sorted, activated withplate-bound anti-CD3/anti-CD28 antibodies in the presence of TGFβ1 andIL-6 for 48 h. Cells were restimulated with PMA/ionomycine for 4 hoursin the presence of Brefeldin A and cytokine production was measuredusing FACS (B); Supernatant were used for ELISA analysis of IL-17 andIL-10 (C); and RNA were purified from cells directly and subject to qPCR(D). FIG. 46E, F shows cells were sorted and cultured as in B, at 48hours, cells were lifted, washed and resuspended in fresh media with nocytokines for an additional 72 h and restimulated. Cytokine productionwas measured by FACS (E) and mRNA was quantified by qPCR (F). FIG. 46G,H show effector memory T cells (CD4⁺CD62L⁻CD44⁺) (G) or Effector memoryTh17 cells (CD4⁺CD62L⁻CD44⁺RorγtGFP⁺) (H) were sorted directly ex vivoand activated with plate-bound anti-CD3/anti-CD28 antibodies for 48hours. Cells were harvested and cultured with PMA/ionomycine for 4 hoursin the presence of Brefeldin A and subject to FACS. Data arerepresentative of at least 3 independent mouse experiments.

FIG. 47A-47F shows CD5L is a major switch that regulates thepathogenicity of Th17 cells. Naïve WT or CD5L^(−/−) 2 D2 T cells weresorted and differentiated with TGFβ1+IL-6 in the presence of irradiatedAPC (Jager, Dardalhon et al. 2009). Cells were rested and reactivatedwith plate-bound anti-CD3 and anti-CD28 antibodies for 48 h andintravenously injected into WT host. (A) Representative FACS plot areshown of cytokine profile of 2D2 T cells after differentiation and priorto in vivo transfer. (B) Weight and EAE score of recipient mice; (C)Representative histology of optic nerve (upper two panels) and CNS(lower panel). Panels are Luxol fast blue-hematoxylin and eosin stains.Demyelination is indicated by loss of normal blue staining of myelin inlower panels of CNS. (D) Representative cytokine profile of WT andCD5L^(−/−) 2 D2 lymphocytes isolated from CNS at day 27 post transfer.Cells were gated on Va3.2⁺CD4⁺. All data are representative of 3independent mouse experiments. (E) Naïve 2D2 WT or CD5L^(−/−) T cellswere sorted and 100,000 cells were transferred into CD45.1 WT host.Recipients were than immunized with MOG/CFA the following day. T cellswere isolated from the draining LN on day 10 following immunization andrestimulated with PMA/ionomycin as described in FIG. 46. RepresentativeFACS plots are gated on CD45.2⁺CD4⁺ cells and are of 2 independentexperiments each with four mice. (F) Naïve T cells were differentiatedwith TGFβ1+IL-6 as in FIG. 46E and subject to RNA purification and qPCR.Data are summary of at least three independent mouse experiments.

FIG. 48A-48J shows CD5L shifts Th17 cell lipidome balance from saturatedto unsaturated lipid, modulating Rorγt ligand availability and function.FIG. 48A, B shows. Lipidome analysis of Th17 cells. (A) WT andCD5L^(−/−) naïve T cells were differentiated as in FIG. 46B in thepresence of cytokines as indicated. Cells and supernatant were harvestedat 96 hours and subjected to MS/LC. Three independent mouse experimentswere performed. Data shown are median expression of each metaboliteidentified that have at least 1.5 fold differences between and WT andCD5L^(−/−) under the TGFβ1+IL-6 condition. (B) Expression ofrepresentative metabolites including a cholesterol ester and aPUFA-containing TAG species. FIG. 48 C, D, E, F-J show as follows: (C)Metabolomic analysis of independent mouse experiments where T cells weredifferentiated under various cytokine conditions as indicated andharvested at 48 h and 96 h. Summary metabolomics analysis is shown inFIG. 52A. (D,E) Rorγt ChIP from Th17 cells differentiated as describedin A. under various conditions as indicated. F-K. Dual luciferasereporter assay was performed in EL4 cells stably transfected with acontrol vector or Rorγt vector. (F, G) CD5L retroviral vector wascotransfected in F and G at 0, 25, 50 and 100 ng/well. (H-J) 10 μM ofeither arachidonic acid (PUFA) or 20 μM of palmitic acid (SFA) were usedwhenever a single dose was indicated and in titration experiments, 20μM, 4 μM and 0.8 μM for PUFA/SFA and 5 μM, 0.5 μM and 0.05 μM of 7,27-dihydroxycholesterol were used. All ChIP and luciferase assay arerepresentative of at least 3 independent experiments.

FIG. 49 CD5L expression follows the pro-inflammatory/regulatory moduledichotomy across single cells. Shown is a PCA plot (first two PCs) withthe cells differentiated under the TGF-β1+IL-6 condition at 48 h, whereeach cell is colored by an expression ranking score of CD5L (red: high,blue: low) and the first PC is marked by the pro-inflammatory/regulatorymodule dichotomy.

FIG. 50A-50E PUFA and SFA can regulate Th17 cell function and contributeto CD5L-dependent regulation of Th17 cells. (A) Naïve T cells weresorted from either WT or IL-23RGFP reporter mice, activated withplate-bound anti-CD3/anti-CD28 and differentiated with TGFβ1+IL-6 for 48hours. At 48 h, cells were cultured with IL-23 in fresh media in thepresence of either 10 uM arachidonic acid (PUFA) or 20 uM of palmiticacid (SFA) for another 48 hours and harvested for PMA/ionomycinrestimulation and FACS. The concentration of FFA was predetermined intitration experiments (data not shown). (B) Cells from WT and Rorc^(−/−)mice were sorted, differentiated and treated with FFA as in A. Cellswere harvested for RNA purification and qPCR. (C) Naïve WT andCD5L^(−/−) T cells were differentiated as in A. Cells were then lifted,washed and replated in fresh media with no addition of cytokines and inthe presence of control or 5 uM of arachidonic acid (PUFA). Cytokineprofile of T cells were measured after PMA/ionomycin restimulation. Dataare representative of at least 3 independent differentiationexperiments. DE. naïve T cells were sorted and differentiated withTGFβ1+IL-6 as in A. At 48 h, cells were then lifted, washed and replatedin fresh media with no addition of cytokines and in the presence ofcontrol or 5 uM arachidonic acid (PUFA) for CD5L−/− T cells; and controlor 25 uM palmitic acid (SFA) for WT T cells. Another 48 hours later,cells were harvested for nanostring analysis (D) or qPCR (E).

FIG. 51A-51C Model for action of PUFA and CD5L. During differentiation(A) abundant Rorγt ligand are synthesized, limiting the specific impactof PUFA/SFA; once Th17 cells are differentiated (B,C), however, ligandsynthesis is substantially reduced due to decreased glucose metabolism,allowing PUFA to have a more pronounced effect. The extent of thiseffect depends on whether CD5L is present (B) or absent (C), resultingin less or more pathogenic cells, respectively.

FIG. 52A-52D shows characterization of WT and CD5L−/− mice with EAE.Mice were immunized as in FIG. 46A. (A) 15 days post immunization,lymphocytes from CNS were isolated and directly stained and analyzedwith flow cytometry for the expression of FoxP3. (B) Cells from CNS asin A were restimulated with PMA/ionomycin with Brefeldin A for 4 hoursand profiled for cytokine production by flow cytometry. (C) Cells wereisolated from Inguinal LN of mice 10 days after immunization. 3HThymidine incorporation assays was used to determine T cellproliferation in response to MOG35-55 peptide; (D) Supernatant from Cwere harvested amount of IL-17 was determined by ELISA.

FIG. 53A-53D shows CD5L antagonizes pathogenicity of Th17 cells. PassiveEAE is induced as described in FIG. 46. Briefly, naïve 2D2 cells weresorted from WT mice and differentiated with IL-1β+IL-6+IL-23. At 24 h,retroviral supernatant containing either CD5L-GFP overexpression- orcontrol-GFP construct were used to infect the activated cells. Theexpression of CD5L was analyzed at day 3 post-infection. 50% of cellsexpressed GFP in both groups. (A) Representative flow cytometry analysisof cytokine profile prior to transfer; (B) Weight loss curve aftertransfer; (C) EAE score; (D) representative flow cytometry data ofcytokine profile of CD4+ T cells from CNS at day 30 post transfer.

FIG. 54A-54D CD5L regulate lipid metabolism in Th17 cells and modulateRorγt function. (A) Rorγt binding sites in the Il17, Il23r and Il10regions as identified from Rorγt ChIP-seq (Xiao, Yosef et al. 2014). Toprow is isotype control (red) and bottom role shows Rorγt ChIP-seqresults from anti-Rorγt antibody (Experimental Procedures) (B) ChIP-PCRof Rorγt in the genomic region of Il23r as in FIG. 48E. (C,D) Rorγttranscriptional activity was measured with respect to Il123r (C) andIl10 (D) in the presence of retroviral vector expressing Cd5l as in FIG.48G.

DETAILED DESCRIPTION

This invention relates generally to compositions and methods foridentifying the regulatory networks that control T cell balance, T celldifferentiation, T cell maintenance and/or T cell function, as wellcompositions and methods for exploiting the regulatory networks thatcontrol T cell balance, T cell differentiation, T cell maintenanceand/or T cell function in a variety of therapeutic and/or diagnosticindications.

The invention provides compositions and methods for modulating T cellbalance. The invention provides T cell modulating agents that modulate Tcell balance. For example, in some embodiments, the invention provides Tcell modulating agents and methods of using these T cell modulatingagents to regulate, influence or otherwise impact the level of and/orbalance between T cell types, e.g., between Th17 and other T cell types,for example, regulatory T cells (Tregs). For example, in someembodiments, the invention provides T cell modulating agents and methodsof using these T cell modulating agents to regulate, influence orotherwise impact the level of and/or balance between Th17 activity andinflammatory potential. As used herein, terms such as “Th17 cell” and/or“Th17 phenotype” and all grammatical variations thereof refer to adifferentiated T helper cell that expresses one or more cytokinesselected from the group the consisting of interleukin 17A (IL-17A),interleukin 17F (IL-17F), and interleukin 17A/F heterodimer (IL17-AF).As used herein, terms such as “Th1 cell” and/or “Th1 phenotype” and allgrammatical variations thereof refer to a differentiated T helper cellthat expresses interferon gamma (IFNγ). As used herein, terms such as“Th2 cell” and/or “Th2 phenotype” and all grammatical variations thereofrefer to a differentiated T helper cell that expresses one or morecytokines selected from the group the consisting of interleukin 4(IL-4), interleukin 5 (IL-5) and interleukin 13 (IL-13). As used herein,terms such as “Treg cell” and/or “Treg phenotype” and all grammaticalvariations thereof refer to a differentiated T cell that expressesFoxp3.

These compositions and methods use T cell modulating agents to regulate,influence or otherwise impact the level and/or balance between T celltypes, e.g., between Th17 and other T cell types, for example,regulatory T cells (Tregs).

The invention provides methods and compositions for modulating T celldifferentiation, for example, helper T cell (Th cell) differentiation.The invention provides methods and compositions for modulating T cellmaintenance, for example, helper T cell (Th cell) maintenance. Theinvention provides methods and compositions for modulating T cellfunction, for example, helper T cell (Th cell) function. Thesecompositions and methods use T cell modulating agents to regulate,influence or otherwise impact the level and/or balance between Th17 celltypes, e.g., between pathogenic and non-pathogenic Th17 cells. Thesecompositions and methods use T cell modulating agents to influence orotherwise impact the differentiation of a population of T cells, forexample toward the Th17 cell phenotype, with or without a specificpathogenic distinction, or away from the Th17 cell phenotype, with orwithout a specific pathogenic distinction. These compositions andmethods use T cell modulating agents to influence or otherwise impactthe maintenance of a population of T cells, for example toward the Th17cell phenotype, with or without a specific pathogenic distinction, oraway from the Th17 cell phenotype, with or without a specific pathogenicdistinction. These compositions and methods use T cell modulating agentsto influence or otherwise impact the differentiation of a population ofTh17 cells, for example toward the pathogenic Th17 cell phenotype oraway from the pathogenic Th17 cell phenotype, or toward thenon-pathogenic Th17 cell phenotype or away from the non-pathogenic Th17cell phenotype. These compositions and methods use T cell modulatingagents to influence or otherwise impact the maintenance of a populationof Th17 cells, for example toward the pathogenic Th17 cell phenotype oraway from the pathogenic Th17 cell phenotype, or toward thenon-pathogenic Th17 cell phenotype or away from the non-pathogenic Th17cell phenotype. These compositions and methods use T cell modulatingagents to influence or otherwise impact the differentiation of apopulation of T cells, for example toward a non-Th17 T cell subset oraway from a non-Th17 cell subset. These compositions and methods use Tcell modulating agents to influence or otherwise impact the maintenanceof a population of T cells, for example toward a non-Th17 T cell subsetor away from a non-Th17 cell subset.

As used herein, terms such as “pathogenic Th17 cell” and/or “pathogenicTh17 phenotype” and all grammatical variations thereof refer to Th17cells that, when induced in the presence of TGF-β3, express an elevatedlevel of one or more genes selected from Cxcl3, IL22, IL3, Ccl4, Gzmb,Lrmp, Ccl5, Casp1, Csf2, Ccl3, Tbx21, Icos, IL17r, Stat4, Lgals3 andLag, as compared to the level of expression in a TGF-β3-induced Th17cells. As used herein, terms such as “non-pathogenic Th17 cell” and/or“non-pathogenic Th17 phenotype” and all grammatical variations thereofrefer to Th17 cells that, when induced in the presence of TGF-β3,express a decreased level of one or more genes selected from IL6st,IL1rn, Ikzf3, Maf, Ahr, IL9 and IL10, as compared to the level ofexpression in a TGF-β3-induced Th17 cells.

These compositions and methods use T cell modulating agents to influenceor otherwise impact the function and/or biological activity of a T cellor T cell population. These compositions and methods use T cellmodulating agents to influence or otherwise impact the function and/orbiological activity of a helper T cell or helper T cell population.These compositions and methods use T cell modulating agents to influenceor otherwise impact the function and/or biological activity of a Th17cell or Th17 cell population. These compositions and methods use T cellmodulating agents to influence or otherwise impact the function and/orbiological activity of a non-Th17 T cell or non-Th17 T cell population,such as, for example, a Treg cell or Treg cell population, or anotherCD4+ T cell or CD4+ T cell population. These compositions and methodsuse T cell modulating agents to influence or otherwise impact theplasticity of a T cell or T cell population, e.g., by converting Th17cells into a different subtype, or into a new state.

The methods provided herein combine transcriptional profiling at hightemporal resolution, novel computational algorithms, and innovativenanowire-based tools for performing perturbations in primary T cells tosystematically derive and experimentally validate a model of the dynamicregulatory network that controls Th17 differentiation. See e.g., Yosefet al., “Dynamic regulatory network controlling Th17 celldifferentiation, Nature, vol. 496: 461-468 (2013)/doi:10.1038/nature11981, the contents of which are hereby incorporated byreference in their entirety. The network consists of twoself-reinforcing, but mutually antagonistic, modules, with novelregulators, whose coupled action may be essential for maintaining thelevel and/or balance between Th17 and other CD4+ T cell subsets.Overall, 9,159 interactions between 71 regulators and 1,266 genes wereactive in at least one network; 46 of the 71 are novel. The examplesprovided herein identify and validate 39 regulatory factors, embeddingthem within a comprehensive temporal network and reveals itsorganizational principles, and highlights novel drug targets forcontrolling Th17 differentiation.

A “Th17-negative” module includes regulators such as SP4, ETS2, IKZF4,TSC22D3 and/or, IRF1. It was found that the transcription factorTsc22d3, which acts as a negative regulator of a defined subtype of Th17cells, co-localizes on the genome with key Th17 regulators. The “Th17positive” module includes regulators such as MINA, PML, POU2AF1, PROCR,SMARCA4, ZEB1, EGR2, CCR6, and/or FAS. Perturbation of the chromatinregulator Mina was found to up-regulate Foxp3 expression, perturbationof the co-activator Pou2af1 was found to up-regulate IFN-γ production instimulated naïve cells, and perturbation of the TNF receptor Fas wasfound to up-regulate IL-2 production in stimulated naïve cells. Allthree factors also control IL-17 production in Th17 cells. Effectivecoordination of the immune system requires careful balancing of distinctpro-inflammatory and regulatory CD4+ helper T cell populations. Amongthose, pro-inflammatory IL-17 producing Th17 cells play a key role inthe defense against extracellular pathogens and have also beenimplicated in the induction of several autoimmune diseases (see e.g.,Bettelli, E., Oukka, M. & Kuchroo, V. K. T(H)-17 cells in the circle ofimmunity and autoimmunity. Nat Immunol 8, 345-350,doi:10.1038/ni0407-345 (2007)), including for example, psoriasis,ankylosing spondylitis, multiple sclerosis and inflammatory boweldisease. Th17 differentiation from naïve T-cells can be triggered invitro by the cytokines TGF-β1 and IL-6. While TGF-β1 alone inducesFoxp3+ regulatory T cells (iTreg) (see e.g., Zhou, L. et al.TGF-beta-induced Foxp3 inhibits T(H)17 cell differentiation byantagonizing RORgammat function. Nature 453, 236-240, doi:nature06878[pii]10.1038/nature06878 (2008)), the presence of IL-6 inhibits iTregand induces Th17 differentiation (Bettelli et al., Nat Immunol 2007).

While TGF-β1 is required for the induction of Foxp3+ induced Tregs(iTregs), the presence of IL-6 inhibits the generation of iTregs andinitiates the Th17 differentiation program. This led to the hypothesisthat a reciprocal relationship between pathogenic Th17 cells and Tregcells exists (Bettelli et al., Nat Immunol 2007), which may depend onthe balance between the mutually antagonistic master transcriptionfactors (TFs) ROR-γt (in Th17 cells) and Foxp3 (in Treg cells) (Zhou etal., Nature 2008). Other cytokine combinations have also been shown toinduce ROR-γt and differentiation into Th17 cells, in particular TGF-β1and IL-21 or IL-1β, TGF-β3+IL-6, IL-6, and IL-23 (Ghoreschi, K. et al.Generation of pathogenic T(H)17 cells in the absence of TGF-betasignaling. Nature 467, 967-971, doi:10.1038/nature09447 (2010)).Finally, although a number of cytokine combinations can induce Th17cells, exposure to IL-23 is critical for both stabilizing the Th17phenotype and the induction of pathogenic effector functions in Th17cells.

Much remains unknown about the regulatory network that controls Th17cells (O'Shea, J. et al. Signal transduction and Th17 celldifferentiation. Microbes Infect 11, 599-611 (2009); Zhou, L. & Littman,D. Transcriptional regulatory networks in Th17 cell differentiation.Curr Opin Immunol 21, 146-152 (2009)). Developmentally, as TGF-β isrequired for both Th17 and iTreg differentiation, it is not understoodhow balance is achieved between them or how IL-6 biases toward Th17differentiation (Bettelli et al., Nat Immunol 2007). Functionally, it isunclear how the pro-inflammatory status of Th17 cells is held in checkby the immunosuppressive cytokine IL-10 (O'Shea et al., Microbes Infect2009; Zhou & Littman, Curr Opin Immunol 2009). Finally, many of the keyregulators and interactions that drive development of Th17 remainunknown (Korn, T., Bettelli, E., Oukka, M. & Kuchroo, V. K. IL- and Th17Cells. Annu Rev Immunol 27, 485-517,doi:10.1146/annurev.immunol.021908.13271010.1146/annurev.immunol.021908.132710 [pii] (2009)).

Recent studies have demonstrated the power of coupling systematicprofiling with perturbation for deciphering mammalian regulatorycircuits (Amit, I. et al. Unbiased reconstruction of a mammaliantranscriptional network mediating pathogen responses. Science 326,257-263, doi:10.1126/science.1179050 (2009); Novershtern, N. et al.Densely interconnected transcriptional circuits control cell states inhuman hematopoiesis. Cell 144, 296-309, doi:10.1016/j.cell.2011.01.004(2011); Litvak, V. et al. Function of C/EBPdelta in a regulatory circuitthat discriminates between transient and persistent TLR4-inducedsignals. Nat. Immunol. 10, 437-443, doi:10.1038/ni.1721 (2009); Suzuki,H. et al. The transcriptional network that controls growth arrest anddifferentiation in a human myeloid leukemia cell line. Nat Genet 41,553-562 (2009); Amit, I., Regev, A. & Hacohen, N. Strategies to discoverregulatory circuits of the mammalian immune system. Nature reviews.Immunology 11, 873-880, doi:10.1038/nri3109 (2011); Chevrier, N. et al.Systematic discovery of TLR signaling components delineatesviral-sensing circuits. Cell 147, 853-867,doi:10.1016/j.cell.2011.10.022 (2011); Garber, M. et al. AHigh-Throughput Chromatin Immunoprecipitation Approach RevealsPrinciples of Dynamic Gene Regulation in Mammals. Molecular cell,doi:10.1016/j.molcel.2012.07.030 (2012)). Most of these studies haverelied upon computational circuit-reconstruction algorithms that assumeone ‘fixed’ network. Th17 differentiation, however, spans several days,during which the components and wiring of the regulatory network likelychange. Furthermore, naïve T cells and Th17 cells cannot be transfectedeffectively in vitro by traditional methods without changing theirphenotype or function, thus limiting the effectiveness of perturbationstrategies for inhibiting gene expression.

These limitations are addressed in the studies presented herein bycombining transcriptional profiling, novel computational methods, andnanowire-based siRNA delivery (Shalek, A. K. et al. Vertical siliconnanowires as a universal platform for delivering biomolecules intoliving cells. Proc. Natl. Acad. Sci. U.S.A. 107, 1870-1875,doi:10.1073/pnas.0909350107 (2010) (FIG. 1a) to construct and validatethe transcriptional network of Th17 differentiation. Using genome-wideprofiles of mRNA expression levels during differentiation, a model ofthe dynamic regulatory circuit that controls Th17 differentiation,automatically identifying 25 known regulators and nominating 46 novelregulators that control this system, was built. Silicon nanowires wereused to deliver siRNA into naïve T cells (Shalek et al., Proc. Natl.Acad. Sci. U.S.A. 2010) to then perturb and measure the transcriptionaleffect of 29 candidate transcriptional regulators and 10 candidatereceptors on a representative gene signature at two time points duringdifferentiation. Combining this data, a comprehensive validated model ofthe network was constructed. In particular, the circuit includes 12novel validated regulators that either suppress or promote Th17development. The reconstructed model is organized into two coupled,antagonistic, and densely intra-connected modules, one promoting and theother suppressing the Th17 program. The model highlights 12 novelregulators, whose function was further characterized by their effects onglobal gene expression, DNA binding profiles, or Th17 differentiation inknockout mice. The studies provided herein demonstrate an unbiasedsystematic and functional approach to understanding the development ofthe Th17 T cell subset.

The methods provided herein combine a high-resolution transcriptionaltime course, novel methods to reconstruct regulatory networks, andinnovative nanotechnology to perturb T cells, to construct and validatea network model for Th17 differentiation. The model consists of threeconsecutive, densely intra-connected networks, implicates 71 regulators(46 novel), and suggests substantial rewiring in 3 phases. The 71regulators significantly overlap with genes genetically associated withinflammatory bowel disease (Jostins, L. et al. Host-microbe interactionshave shaped the genetic architecture of inflammatory bowel disease.Nature 491, 119-124, doi:10.1038/nature11582 (2012)) (11 of 71, p<10⁻⁹).Building on this model, 127 putative regulators (80 novel) weresystematically ranked, and top ranking ones were tested experimentally.

It was found that the Th17 regulators are organized into two tightlycoupled, self-reinforcing but mutually antagonistic modules, whosecoordinated action may explain how the balance between Th17, Treg, andother effector T cell subsets is maintained, and how progressivedirectional differentiation of Th17 cells is achieved. Within the twomodules are 12 novel factors (FIGS. 4 and 5), which were furthercharacterized, highlighting four of the factors (others are in FIG. 17A,17B). This validated model highlights at least 12 novel regulators thateither positively or negatively impact the Th17 program (FIGS. 4 and 5).Remarkably, these and known regulators are organized in two tightlycoupled, self-reinforcing and mutually antagonistic modules, whosecoordinated action may explain how the balance between Th17, Treg, andother effector T cells is maintained, and how progressive directionaldifferentiation of Th17 cells is achieved while repressingdifferentiation of other T cell subsets. The function of four of the 12regulators—Mina, Fas, Pou2af1, and Tsc22d3—was further validated andcharacterized by undertaking Th17 differentiation of T cells fromcorresponding knockout mice or with ChIP-Seq binding profiles.

The T cell modulating agents are used to modulate the expression of oneor more target genes or one or more products of one or more target genesthat have been identified as genes responsive to Th17-relatedperturbations. These target genes are identified, for example, bycontacting a T cell, e.g., naïve T cells, partially differentiated Tcells, differentiated T cells and/or combinations thereof, with a T cellmodulating agent and monitoring the effect, if any, on the expression ofone or more signature genes or one or more products of one or moresignature genes. In some embodiments, the one or more signature genesare selected from those listed in Table 1 or Table 2 shown below.

TABLE 1 Signature Genes IL17A IL21R CCL1 PSTPIP1 IL7R BCL3 CD247 IER3IRF4 DPP4 PROCR FZD7 CXCL10 TGFBR1 RELA GLIPR1 IL12RB1 CD83 HIF1A AIM1TBX21 RBPJ PRNP CD4 ZNF281 CXCR3 IL17RA LMNB1 IL10RA NOTCH2 STAT1 MGLLCXCR4 CCL4 LRRFIP1 LSP1 TNFRSF13B TAL2 KLRD1 GJA1 ACVR1B IL9 RUNX1LGALS3BP TGIF1 FAS ID2 ARHGEF3 ABCG2 SPRY1 STAT5A BCL2L11 REL PRF1TNFRSF25 TGM2 ID3 FASLG BATF UBIAD1 ZEB1 MT2A KAT2B MAP3K5 MYD88 POU2AF1NFATC2 RAB33A EGR2 IFNG CD70 CASP1 AES PLAC8 LITAF FOXP1 PML IL17FIL27RA MTA3 TGFBR3 DDR1 IL22 IFIH1 CCR8 IL4 MINA RASGRP1 ZFP161 CD28XBP1 XRCC5 IRF1 TNFSF9 PRDM1 NCF1C CCR6 SMARCA4 AHR NUDT4 SMOX YAX2SLAMF7 PDCD1LG2 ITGB1 IL21 IL1RN PYCR1 CASP6 SAP30 MBNL3 AQP3 NFKBIE CD9ARID5A SEMA7A LAMP2 IL24 TRIM24 PRC1 GATA3 STAT5B CSF2 IFIT1 RORA SKINFE2L2 DNTT SGK1 BCL6 IL23R PMEPA1 IL2RA ELK3 KLF6 GAP43 MT1A CD74ACVR2A PRICKLE1 JAK3 STAT6 NR3C1 OAS2 IL4R TNFSF8 CCR4 ERRFI1 NAMPT IL3CXCR5 LAD1 ITGA3 TGFB1 SKAP2 TMEM126A TGFB3 ETV6 PLEKHF2 LILRB1, LILRB2,LILRB3, INHBA CASP4 STAT2 KATNA1 KLF7 CEBPB IRF7 B4GALT1 RUNX3 TRAF3FLI1 ANXA4 NFKBIZ TRPS1 IRF9 SULT2B1 SERPINE2 JUN GFI1 PHLDA1 RXRA STAT4MXI1 PRKD3 SERTAD1 CMTM6 IFI35 TAP1 MAF SOCS3 MAX TRIM5 IL10 TSC22D3ZNF238 FLNA BMPR1A LIF CHD7 GUSB PTPRJ DAXX FOXM1 C14ORF83 STAT3 KLF9BCL11B VAV3 CCR5 IL6ST RUNX2 ARL5A CCL20 CLCF1 EMP1 GRN SPP1 NFIL3 PELI2PRKCA CD80 IKZF4 SEMA4D PECI RORC ISG20 STARD10 ARMCX2 SERPINB1 CD86TIMP2 SLC2A1 IL12RB2 IL2RB KLF10 RPP14 IFNGR2 NCOA1 CTSW PSMB9 SMAD3NOTCH1 GEM CASP3 FOXP3 TNFRSF12A TRIM25 TRAT1 CD24 CD274 HLA-A PLAGL1CD5L MAFF MYST4 RAD51AP1 CD2 ATF4 FRMD4B NKG7 TNFSF11 ARNTL RFK IFITM2ICOS IL1R1 CD44 HIP1R IRF8 FOXO1 ERCC5

TABLE 2 Subset of Signature Genes AHR HIF1A IRF4 REL ARID5A ICOS IRF8RORA BATF ID2 ITGA3 RORC CASP4 ID3 KLF6 SERPINB1 CASP6 IFNG KLRD1 SGK1CCL20 IL10 LIF SKAP2 CCL4 IL10RA LTA SKI CCR5 IL17A MAF SMOX CCR6 IL17FMAFF SOCS3 CD24 IL17RA MINA STAT1 CD5L IL2 MYC STAT3 CD80 IL21 NFATC2STAT4 CEBPB IL21R NFE2L2 TBX21 CLCF1 IL22 NFIL3 TGFBR1 CSF2 IL23R NOTCH1TGIF1 CXCR3 IL24 NUDT4 TNFRSF25 EGR2 IL2RA PML TNFSF8 ELK3 IL7R POU2AF1TRIM24 ETV6 IL9 PROCR TRPS1 FAS INHBA PSMB9 TSC22D3 FOXP3 IRF1 RBPJZFP36L1 GATA3

In some embodiments, the target gene is one or more Th17-associatedcytokine(s) or receptor molecule(s) selected from those listed in Table3. In some embodiments, the target gene is one or more Th17-associatedtranscription regulator(s) selected from those shown in Table 4.

TABLE 3 Th17-Associated Receptor Molecules ACVR1B CXCR4 IL6ST PROCRACVR2A CXCR5 IL7R PTPRJ BMPR1A DDR1 IRAK1BP1 PVR CCR4 FAS ITGA3 TLR1CCR5 IL15RA KLRD1 TNFRSF12A CCR6 IL18R1 MYD88 TNFRSF13B CCR8 IL1RN PLAURTRAF3 CXCR3

TABLE 4 Th17-Associated Transcription Regulators TRPS1 SMARCA4 CDYLSIRT2 SMOX ZFP161 IKZF4 MAFF ARNTL TP53 NCOA1 CHMP1B UBE2B SUZ12 SS18GATAD2B NR3C1 POU2AF1 PHF13 ZNF703 TRIM24 MYST4 MTA3 ZNRF1 FLI1 MXI1ASXL1 JMJD1C SP4 CHD7 LASS4 ZFP36L2 EGR2 CREB3L2 SKIL TSC22D4 ZNF281VAX2 FOSL2 NFE2L2 RELA KLF10 RUNX2 RNF11 IRF7 SKI TLE1 ARID3A STAT2 ELK3ELL2 MEN1 IRF3 ZEB1 BCL11B CBX4 XBP1 LRRFIP1 KAT2B ZFP62 PRDM1 PAXBP1KLF6 CIC ATF4 ID1 E2F8 HCLS1 CREB1 ZNF238 ZNRF2 ZFP36L1 IRF9 VAV1TSC22D3 TGIF1 IRF2 MINA HMGB2 FOXJ2 BATF3 FUS

In some embodiments, the target gene is one or more Th17-associatedtranscription regulator(s) selected from those shown in Table 5. In someembodiments, the target gene is one or more Th17-associated receptormolecule(s) selected from those listed in Table 6. In some embodiments,the target gene is one or more Th17-associated kinase(s) selected fromthose listed in Table 7. In some embodiments, the target gene is one ormore Th17-associated signaling molecule(s) selected from those listed inTable 8. In some embodiments, the target gene is one or moreTh17-associated receptor molecule(s) selected from those listed in Table9.

TABLE 5 Candidate Regulators % Interactions OR differential expression(compared to Th0) IL23R knockout Symbol Early Intermediate Late (late)IRF4 0.892473118 0.841397849 1 UNDER-EXPR IFI35 1 0.9523809520.904761905 UNDER-EXPR ETS1 1 0.636363636 0.636363636 UNDER-EXPR NMI 10.857142857 0 UNDER-EXPR SAP18 0.785714286 0.928571429 1 OVER-EXPR FLI11 0.971590909 0.869318182 SP4 1 0.710900474 0.63507109 UNDER-EXPR SP1001 0 0 UNDER-EXPR TBX21 0 1 0 OVER-EXPR POU2F2 0 1 0 OVER-EXPR ZNF281 0 10 UNDER-EXPR NFIL3 0.611111111 0.611111111 1 SMARCA4 0.8058252430.757281553 1 OVER-EXPR CSDA 0 0 1 OVER-EXPR STAT3 0.8553921570.970588235 1 UNDER-EXPR FOXO1 0.875 1 0.875 NCOA3 0.875 1 0.9375 LEF10.380952381 0.904761905 1 UNDER-EXPR SUZ12 0 1 0 OVER-EXPR CDC5L 0 1 0UNDER-EXPR CHD7 1 0.860465116 0.686046512 UNDER-EXPR HIF1A 0.7333333330.666666667 1 UNDER-EXPR RELA 0.928571429 1 0.880952381 UNDER-EXPR STAT21 0.821428571 0 STAT5B 1 0.848484848 0.515151515 UNDER-EXPR RORC 0 0 1UNDER-EXPR STAT1 1 0.635658915 0 UNDER-EXPR MAZ 0 1 0 LRRFIP1 0.9 0.8 1REL 1 0 0 OVER-EXPR CITED2 1 0 0 UNDER-EXPR RUNX1 0.9251497010.925149701 1 UNDER-EXPR ID2 0.736842105 0.789473684 1 SATB1 0.4523809520.5 1 UNDER-EXPR TRIM28 0 1 0 STAT6 0.54 0.64 1 OVER-EXPR STAT5A 00.642241379 1 UNDER-EXPR BATF 0.811732606 0.761255116 1 UNDER-EXPR EGR10.857142857 1 0 OVER-EXPR EGR2 0.896428571 0.839285714 1 OVER-EXPR AES0.888888889 1 0.777777778 IRF8 0 1 0.824786325 OVER-EXPR SMAD20.806060606 0.781818182 1 NFKB1 0.266666667 0.706666667 1 UNDER-EXPRPHF21A 1 0.533333333 0.933333333 UNDER-EXPR CBFB 0.35 0.9 1 ZFP1610.818181818 0.714876033 1 OVER-EXPR ZEB2 0 0.411764706 1 SP1 00.740740741 1 FOXJ2 0 1 1 IRF1 1 0 0 MYC 0 0.595505618 1 UNDER-EXPR IRF21 0 0 EZH1 1 0.8 0.44 UNDER-EXPR RUNX2 0 0 1 JUN 0.647058824 0.6470588241 OVER-EXPR STAT4 1 0 0 UNDER-EXPR MAX 0.947368421 0.789473684 1 TP530.292307692 0.615384615 1 UNDER-EXPR IRF3 1 0.485294118 0.235294118UNDER-EXPR BCL11B 0.666666667 0.611111111 1 E2F1 0 0 1 OVER-EXPR IRF9 10.440433213 0 UNDER-EXPR GATA3 1 0 0 OVER-EXPR TRIM24 0.965517241 10.965517241 UNDER-EXPR E2F4 0.083333333 0.5 1 NR3C1 1 1 0 UNDER-EXPRETS2 1 0.925925926 0.864197531 OVER-EXPR CREB1 0.802197802 0.706959707 1IRF7 1 0.777777778 0 OVER-EXPR TFEB 0.8 0.6 1 TRPS1 OVER-EXPR UNDER-EXPRSMOX OVER-EXPR OVER-EXPR UNDER-EXPR RORA OVER-EXPR OVER-EXPR UNDER-EXPRARID5A OVER-EXPR OVER-EXPR OVER-EXPR OVER-EXPR ETV6 OVER-EXPR OVER-EXPRARNTL OVER-EXPR UNDER-EXPR UBE2B OVER-EXPR UNDER-EXPR XBP1 OVER-EXPRPRDM1 OVER-EXPR OVER-EXPR UNDER-EXPR ATF4 OVER-EXPR OVER-EXPR POU2AF1OVER-EXPR UNDER-EXPR CEBPB OVER-EXPR OVER-EXPR UNDER-EXPR CREM OVER-EXPROVER-EXPR UNDER-EXPR MYST4 OVER-EXPR OVER-EXPR UNDER-EXPR MXI1 OVER-EXPRUNDER-EXPR RBPJ OVER-EXPR OVER-EXPR OVER-EXPR CREB3L2 OVER-EXPROVER-EXPR UNDER-EXPR VAX2 OVER-EXPR OVER-EXPR KLF10 OVER-EXPR OVER-EXPRSKI OVER-EXPR OVER-EXPR UNDER-EXPR ELK3 OVER-EXPR OVER-EXPR ZEB1OVER-EXPR OVER-EXPR OVER-EXPR PML OVER-EXPR OVER-EXPR UNDER-EXPR SERTAD1OVER-EXPR UNDER-EXPR NOTCH1 OVER-EXPR OVER-EXPR OVER-EXPR AHR OVER-EXPROVER-EXPR OVER-EXPR UNDER-EXPR C21ORF66 OVER-EXPR UNDER-EXPR SAP30OVER-EXPR OVER-EXPR ID1 OVER-EXPR OVER-EXPR OVER-EXPR ZNF238 OVER-EXPROVER-EXPR VAV1 OVER-EXPR UNDER-EXPR MINA OVER-EXPR OVER-EXPR UNDER-EXPRBATF3 OVER-EXPR OVER-EXPR CDYL UNDER-EXPR IKZF4 OVER-EXPR OVER-EXPROVER-EXPR OVER-EXPR NCOA1 OVER-EXPR OVER-EXPR BCL3 OVER-EXPR OVER-EXPROVER-EXPR UNDER-EXPR JUNB OVER-EXPR UNDER-EXPR SS18 OVER-EXPR OVER-EXPRPHF13 OVER-EXPR MTA3 OVER-EXPR UNDER-EXPR ASXL1 OVER-EXPR OVER-EXPRLASS4 OVER-EXPR UNDER-EXPR SKIL OVER-EXPR OVER-EXPR OVER-EXPR DDIT3OVER-EXPR OVER-EXPR FOSL2 OVER-EXPR OVER-EXPR TLE1 OVER-EXPR OVER-EXPRATF3 OVER-EXPR ELL2 OVER-EXPR OVER-EXPR OVER-EXPR JARID2 OVER-EXPROVER-EXPR KLF9 OVER-EXPR OVER-EXPR OVER-EXPR KAT2B OVER-EXPR UNDER-EXPRKLF6 OVER-EXPR OVER-EXPR UNDER-EXPR E2F8 OVER-EXPR OVER-EXPR OVER-EXPRBCL6 OVER-EXPR UNDER-EXPR ZNRF2 UNDER-EXPR TSC22D3 OVER-EXPR UNDER-EXPRKLF7 OVER-EXPR HMGB2 OVER-EXPR FUS OVER-EXPR OVER-EXPR SIRT2 OVER-EXPRMAFF OVER-EXPR OVER-EXPR OVER-EXPR CHMP1B OVER-EXPR UNDER-EXPR GATAD2BOVER-EXPR OVER-EXPR SMAD7 OVER-EXPR OVER-EXPR ZNF703 OVER-EXPR OVER-EXPRZNRF1 OVER-EXPR OVER-EXPR JMJD1C OVER-EXPR UNDER-EXPR ZFP36L2 OVER-EXPRUNDER-EXPR TSC22D4 NFE2L2 OVER-EXPR OVER-EXPR OVER-EXPR UNDER-EXPR RNF11OVER-EXPR ARID3A OVER-EXPR OVER-EXPR UNDER-EXPR MEN1 OVER-EXPR OVER-EXPRRARA OVER-EXPR OVER-EXPR UNDER-EXPR CBX4 OVER-EXPR OVER-EXPR OVER-EXPRZFP62 OVER-EXPR CIC OVER-EXPR HCLS1 UNDER-EXPR ZFP36L1 UNDER-EXPR TGIF1UNDER-EXPR SMAD4 OVER-EXPR IL7R OVER EXPR OVER EXPR UNDER EXPR ITGA3OVER EXPR OVER EXPR IL1R1 OVER EXPR OVER EXPR UNDER EXPR FAS OVER EXPRUNDER EXPR CCR5 OVER EXPR OVER EXPR OVER EXPR UNDER EXPR CCR6 OVER EXPROVER EXPR ACVR2A OVER EXPR OVER EXPR UNDER EXPR IL6ST OVER EXPR OVEREXPR UNDER EXPR IL17RA OVER EXPR OVER EXPR UNDER EXPR CCR8 OVER EXPRDDR1 OVER EXPR OVER EXPR UNDER EXPR PROCR OVER EXPR OVER EXPR OVER EXPRIL2RA OVER EXPR OVER EXPR OVER EXPR OVER EXPR IL12RB2 OVER EXPR OVEREXPR UNDER EXPR MYD88 OVER EXPR OVER EXPR UNDER EXPR BMPR1A OVER EXPRUNDER EXPR PTPRJ OVER EXPR OVER EXPR OVER EXPR TNFRSF13 OVER EXPR OVEREXPR UNDER EXPR CXCR3 OVER EXPR UNDER EXPR IL1RN OVER EXPR OVER EXPRUNDER EXPR CXCR5 OVER EXPR OVER EXPR OVER EXPR UNDER EXPR CCR4 OVER EXPROVER EXPR UNDER EXPR IL4R OVER EXPR OVER EXPR UNDER EXPR IL2RB OVER EXPROVER EXPR TNFRSF12 OVER EXPR OVER EXPR OVER EXPR CXCR4 OVER EXPR OVEREXPR UNDER EXPR KLRD1 OVER EXPR OVER EXPR IRAK1BP1 OVER EXPR OVER EXPRPVR OVER EXPR OVER EXPR OVER EXPR UNDER EXPR IL15RA OVER EXPR OVER EXPRTLR1 OVER EXPR ACVR1B OVER EXPR OVER EXPR IL12RB1 OVER EXPR OVER EXPROVER EXPR IL18R1 OVER EXPR OVER EXPR TRAF3 OVER EXPR OVER EXPR IFNGR1OVER EXPR UNDER EXPR PLAUR OVER EXPR OVER EXPR IL21R UNDER EXPR IL23ROVER EXPR UNDER EXPR

TABLE 6 Candidate Receptor Molecules % Differential expression (comparedto Th0) IL23R knockout Symbol Early Intermediate Late (late) PTPLA UNDEREXPR PSTPIP1 OVER EXPR OVER EXPR UNDER EXPR TK1 UNDER EXPR EIF2AK2 OVEREXPR PTEN UNDER EXPR BPGM UNDER EXPR DCK OVER EXPR PTPRS OVER EXPRPTPN18 OVER EXPR MKNK2 OVER EXPR PTPN1 OVER EXPR UNDER EXPR PTPRE UNDEREXPR SH2D1A OVER EXPR DUSP22 OVER EXPR PLK2 OVER EXPR DUSP6 UNDER EXPRCDC25B UNDER EXPR SLK OVER EXPR UNDER EXPR MAP3K5 UNDER EXPR BMPR1A OVEREXPR UNDER EXPR ACP5 OVER EXPR OVER EXPR UNDER EXPR TXK OVER EXPR OVEREXPR UNDER EXPR RIPK3 OVER EXPR OVER EXPR UNDER EXPR PPP3CA OVER EXPRPTPRF OVER EXPR OVER EXPR OVER EXPR PACSIN1 OVER EXPR NEK4 OVER EXPRUNDER EXPR PIP4K2A UNDER EXPR PPME1 OVER EXPR OVER EXPR UNDER EXPR SRPK2UNDER EXPR DUSP2 OVER EXPR PHACTR2 OVER EXPR OVER EXPR HK2 OVER EXPROVER EXPR DCLK1 OVER EXPR PPP2R5A UNDER EXPR RIPK1 OVER EXPR UNDER EXPRGK OVER EXPR RNASEL OVER EXPR OVER EXPR GMFG OVER EXPR OVER EXPR OVEREXPR STK4 UNDER EXPR HINT3 OVER EXPR DAPP1 OVER EXPR UNDER EXPR TEC OVEREXPR OVER EXPR OVER EXPR UNDER EXPR GMFB OVER EXPR OVER EXPR PTPN6 UNDEREXPR RIPK2 UNDER EXPR PIM1 OVER EXPR OVER EXPR OVER EXPR NEK6 OVER EXPROVER EXPR UNDER EXPR ACVR2A OVER EXPR OVER EXPR UNDER EXPR AURKB UNDEREXPR FES OVER EXPR OVER EXPR ACVR1B OVER EXPR OVER EXPR CDK6 OVER EXPROVER EXPR UNDER EXPR ZAK OVER EXPR OVER EXPR UNDER EXPR VRK2 UNDER EXPRMAP3K8 OVER EXPR UNDER EXPR DUSP14 OVER EXPR UNDER EXPR SGK1 OVER EXPROVER EXPR OVER EXPR UNDER EXPR PRKCQ OVER EXPR UNDER EXPR JAK3 OVER EXPRUNDER EXPR ULK2 OVER EXPR UNDER EXPR HIPK2 OVER EXPR OVER EXPR PTPRJOVER EXPR OVER EXPR OVER EXPR SPHK1 OVER EXPR INPP1 UNDER EXPR TNK2 OVEREXPR OVER EXPR OVER EXPR PCTK1 OVER EXPR OVER EXPR OVER EXPR DUSP1 OVEREXPR NUDT4 UNDER EXPR MAP4K3 OVER EXPR TGFBR1 OVER EXPR OVER EXPR OVEREXPR PTP4A1 OVER EXPR HK1 OVER EXPR OVER EXPR DUSP16 OVER EXPR UNDEREXPR AMP32A OVER EXPR DDR1 OVER EXPR OVER EXPR UNDER EXPR ITK UNDER EXPRWNK1 UNDER EXPR NAGK OVER EXPR UNDER EXPR STK38 OVER EXPR BMP2K OVEREXPR OVER EXPR OVER EXPR OVER EXPR BUB1 UNDER EXPR AAK1 OVER EXPR SIK1OVER EXPR DUSP10 OVER EXPR UNDER EXPR PRKCA OVER EXPR PIM2 OVER EXPRUNDER EXPR STK17B OVER EXPR UNDER EXPR TK2 UNDER EXPR STK39 OVER EXPRALPK2 OVER EXPR OVER EXPR UNDER EXPR MST4 OVER EXPR PHLPP1 UNDER EXPR

TABLE 7 Candidate Kinases % Differential expression (compared to Th)IL23R knockout Symbol Early Intermediate Late (late) SGK1 OVER EXPR OVEREXPR OVER EXPR UNDER EXPR HK2 OVER EXPR OVER EXPR OVER EXPR PRPS1 UNDEREXPR CAMK4 ZAP70 TXK OVER EXPR OVER EXPR OVER EXPR UNDER EXPR NEK6 OVEREXPR OVER EXPR MAPKAPK2 OVER EXPR MFHAS1 UNDER EXPR OVER EXPR PDXK PRKCHOVER EXPR UNDER EXPR CDK6 OVER EXPR OVER EXPR ZAK OVER EXPR OVER EXPRUNDER EXPR PKM2 OVER EXPR JAK2 OVER EXPR UNDER EXPR UNDER EXPR STK38UNDER EXPR UNDER EXPR OVER EXPR ADRBK1 PTK2B UNDER EXPR DGUOK UNDER EXPRUNDER EXPR DGKA UNDER EXPR RIPK3 OVER EXPR OVER EXPR UNDER EXPR PIM1OVER EXPR OVER EXPR OVER EXPR CDK5 STK17B OVER EXPR CLK3 CLK1 ITK UNDEREXPR AKT1 UNDER EXPR PGK1 TWF1 LIMK2 RFK UNDER EXPR WNK1 UNDER EXPR OVEREXPR HIPK1 AXL OVER EXPR UNDER EXPR UNDER EXPR RPS6KB1 CDC42BPA STK38LPRKCD PDK3 PI4KA PNKP CDKN3 STK19 PRPF4B UNDER EXPR MAP4K2 PDPK1 VRK1TRRAP

TABLE 8 Candidate Signaling Molecules From Single Cell Analysis %Differential expression (compared to Th) IL23R knockout Symbol EarlyIntermediate Late (late) CTLA4 OVER EXPR OVER EXPR UNDER EXPR CD9 UNDEREXPR UNDER EXPR UNDER EXPR IL2RA OVER EXPR OVER EXPR OVER EXPR OVER EXPRCD5L OVER EXPR OVER EXPR OVER EXPR CD24 OVER EXPR OVER EXPR UNDER EXPRCD200 OVER EXPR UNDER EXPR UNDER EXPR OVER EXPR CD53 UNDER EXPR OVEREXPR UNDER EXPR TNFRSF9 UNDER EXPR UNDER EXPR OVER EXPR CD44 UNDER EXPRCD96 UNDER EXPR UNDER EXPR CD83 UNDER EXPR UNDER EXPR IL27RA CXCR3 OVEREXPR OVER EXPR TNFRSF4 UNDER EXPR IL4R OVER EXPR OVER EXPR PROCR OVEREXPR OVER EXPR OVER EXPR LAMP2 OVER EXPR OVER EXPR UNDER EXPR CD74 UNDEREXPR UNDER EXPR OVER EXPR TNFRSF13 OVER EXPR OVER EXPR UNDER EXPR PDCD1UNDER EXPR TNFRSF1B IL21R UNDER EXPR UNDER EXPR IFNGR1 OVER EXPR UNDEREXPR ICOS UNDER EXPR OVER EXPR PTPRC ADAM17 FCGR2B TNFSF9 UNDER EXPRUNDER EXPR UNDER EXPR MS4A6A UNDER EXPR UNDER EXPR UNDER EXPR CCR4 OVEREXPR OVER EXPR CD226 CD3G UNDER EXPR UNDER EXPR ENTPD1 ADAM10 UNDER EXPRUNDER EXPR UNDER EXPR CD27 UNDER EXPR UNDER EXPR UNDER EXPR UNDER EXPRCD84 UNDER EXPR UNDER EXPR ITGAL UNDER EXPR CCND2 UNDER EXPR BSG UNDEREXPR CD40LG PTPRCAP UNDER EXPR UNDER EXPR UNDER EXPR CD68 CD63 SLC3A2HLA-DQA1 OVER EXPR CTSD CSF1R CD3D UNDER EXPR CD247 UNDER EXPR UNDEREXPR CD14 ITGAV FCER1G IL2RG OVER EXPR UNDER EXPR

TABLE 9 Candidate Receptor Molecules From Single Cell Analysis %Differential expression (compared to Th) IL23R knockout Symbol EarlyIntermediate Late (late) PLEK OVER EXPR BHLH40 OVER EXPR OVER EXPRARID5A OVER EXPR OVER EXPR OVER EXPR OVER EXPR ETS1 OVER EXPR OVER EXPRUNDER EXPR IRF4 OVER EXPR OVER EXPR OVER EXPR IKZF3 RORC OVER EXPR OVEREXPR UNDER EXPR STAT4 UNDER EXPR UNDER EXPR UNDER EXPR RORA OVER EXPROVER EXPR UNDER EXPR PHF6 ID3 UNDER EXPR UNDER EXPR UNDER EXPR OVER EXPRZBTB32 UNDER EXPR OVER EXPR IFI35 OVER EXPR ID2 OVER EXPR OVER EXPR OVEREXPR UNDER EXPR MDM4 CHMP2A ANKHD1 CHD7 OVER EXPR OVER EXPR UNDER EXPRSTAT5B OVER EXPR OVER EXPR MAML2 ID1 OVER EXPR OVER EXPR OVER EXPR SS18OVER EXPR MAF ETV6 OVER EXPR OVER EXPR CCRN4L OVER EXPR OVER EXPR NASPBLOC1S1 OVER EXPR XAB2 STAT5A OVER EXPR UNDER EXPR IKZF1 UNDER EXPR JUNBOVER EXPR OVER EXPR THRAP3 OVER EXPR SP100 OVER EXPR PYCR1 OVER EXPROVER EXPR OVER EXPR HMGA1 TAF1B UNDER EXPR CNOT2 NOC4L OVER EXPR SKIUNDER EXPR OVER EXPR OVER EXPR VAV1 OVER EXPR OVER EXPR NR4A2 UNDER EXPRUNDER EXPR OVER EXPR LGTN NFKBIA UNDER EXPR KDM6B MAZ CDC5L UNDER EXPRHCLS1 UNDER EXPR OVER EXPR BAZ2B OVER EXPR MXD3 BATF OVER EXPR OVER EXPRE2F4 NFKBIB RBPJ OVER EXPR OVER EXPR OVER EXPR TOX4 CENPT CASP8AP2 ECE2MIER1 AHR OVER EXPR OVER EXPR OVER EXPR SPOP UNDER EXPR BTG1 MATR3 UNDEREXPR JMJD1C OVER EXPR OVER EXPR HMGB2 OVER EXPR CREG1 OVER EXPR NFATC1NFE2L2 OVER EXPR OVER EXPR OVER EXPR WHSC1L1 TBPL1 TRIP12 BTG2 HMGN1UNDER EXPR ATF2 NR4A3 C16ORF80 MBNL1 UNDER EXPR UNDER EXPR WDHD1 LASS6CREM OVER EXPR OVER EXPR CARM1 RNF5 UNDER EXPR SMARCA4 OVER EXPR GATAD1TCERG1 UNDER EXPR CHRAC1 NFYC ATF3 OVER EXPR OVER EXPR ZNF326 OVER EXPRKLF13 TFDP1 LRRFIP1 OVER EXPR OVER EXPR MORF4L2 FOXN3 HDAC8 MORF4L1DNAJC2 OVER EXPR MAFG YBX1

Among the novel ‘Th17 positive’ factors is the zinc finger E-box bindinghomeobox 1 Zeb1, which is early-induced and sustained in the Th17 timecourse (FIG. 17a ), analogous to the expression of many known key Th17factors. Zeb1 knockdown decreases the expression of Th17 signaturecytokines (including IL-17A, IL-17F, and IL-21) and TFs (including Rbpj,Maff, and Mina) and of late induced cytokine and receptor molecule genes(p<10⁻⁴, cluster C19). It is bound in Th17 cells by ROR-γt, Batf andStat3, and is down-regulated in cells from Stat3 knockout mice (FIG. 17a). Interestingly, Zeb1 is known to interact with the chromatin factorSmarca4/Brg1 to repress the E-cadherin promoter in epithelial cells andinduce an epithelial-mesenchymal transition (Sänchez-Tilló, E. et al.ZEB1 represses E-cadherin and induces an EMT by recruiting the SWI/SNFchromatin-remodeling protein BRG1. Oncogene 29, 3490-3500,doi:10.1038/onc.2010.102 (2010)). Smarca4 is a regulator in all threenetwork models (FIG. 2d,e ) and a member of the ‘positive module’ (FIG.4b ). Although it is not differentially expressed in the Th17 timecourse, it is bound by Batf, Irf4 and Stat3 (positive regulators ofTh17), but also by Gata3 and Stat5 (positive regulators of otherlineages, FIG. 17a ). Chromatin remodeling complexes that containSmarca4 are known to displace nucleosomes and remodel chromatin at theIFN-γ promoter and promote its expression in Th1 cells (Zhang, F. &Boothby, M. T helper type 1-specific Brg1 recruitment and remodeling ofnucleosomes positioned at the IFN-gamma promoter are Stat4 dependent. J.Exp. Med. 203, 1493-1505, doi:10.1084/jem.20060066 (2006)). There arealso potential Smarca4 binding DNA sequences within the vicinity of theIL-17a promoter (Matys, V. et al. TRANSFAC: transcriptional regulation,from patterns to profiles. Nucleic Acids Res. 31, 374-378 (2003)). Takentogether, this suggests a model where chromatin remodeling by Smarca4,possibly in interaction with Zeb1, positive regulates Th17 cells and isessential for IL-17 expression.

Conversely, among the novel ‘Th17 negative’ factors is Sp4, anearly-induced gene, predicted in the model as a regulator of ROR-γt andas a target of ROR-γt, Batf, Irf4, Stat3 and Smarca4 (FIG. 17b ). Sp4knockdown results in an increase in ROR-γt expression at 48 h, and anoverall stronger and “cleaner” Th17 differentiation as reflected by anincrease in the expression of Th17 signature genes, including IL-17,IL-21 and Irf4, and decrease in the expression of signature genes ofother CD4+ cells, including Gata3, Foxp3 and Stat4.

These novel and known regulatory factors act coordinately to orchestrateintra- and intermodules interactions and to promote progressivedifferentiation of Th17 cells, while limiting modules that inhibitdirectional differentiation of this subset and promote differentiationof T cells into other T cell subsets. For instance, knockdown of Smarca4and Zeb1 leads to decrease in Mina (due to all-positive interactionsbetween Th17 ‘positive regulators’), while knockdown of Smarca4 or Minaleads to increase in Tsc22d3 31 expression, due to negative cross-moduleinteractions. As shown using RNAseq, these effects extend beyond theexpression of regulatory factors in the network and globally affect theTh17 transcriptional program: e.g. knock-down of Mina has substantialeffects on the progression of the Th17 differentiation network from theintermediate to the late phase, as some of its affected down-regulatedgenes significantly overlap the respective temporal clusters (p<10⁻⁵,e.g., clusters C9, C19). An opposite trend is observed for the negativeregulators Tsc22d3 and Sp4. For example, the transcriptional regulatorSp4 represses differentiating Th17 cells from entering into the latephase of differentiation by inhibiting the cytokine signaling (C19;p<10⁻⁷) and heamatopoesis (C20; p<10⁻³) clusters, which include Ahr,Batf, ROR-γt, etc. These findings emphasize the power of large-scalefunctional perturbation studies in understanding the action of complexmolecular circuits that govern Th17 differentiation.

In a recent work, Ciofani et al. (Ciofani, M. et al. A ValidatedRegulatory Network for Th17 Cell Specification. Cell,doi:10.1016/j.cell.2012.09.016 (2012)) systematically ranked Th17regulators based on ChIPSeq data for known key factors andtranscriptional profiles in wild type and knockout cells. While theirnetwork centered on known core Th17 TFs, the complementary approachpresented herein perturbed many genes in a physiologically meaningfulsetting. Reassuringly, their core Th17 network significantly overlapswith the computationally inferred model (FIG. 18).

The wiring of the positive and negative modules (FIGS. 4 and 5) uncoverssome of the functional logic of the Th17 program, but likely involveboth direct and indirect interactions. The functional model provides anexcellent starting point for deciphering the underlying physicalinteractions with DNA binding profiles (Glasmacher, E. et al. A GenomicRegulatory Element That Directs Assembly and Function of Immune-SpecificAP-1-IRF Complexes. Science, doi:10.1126/science.1228309 (2012)) orprotein-protein interactions (Wu, C., Yosef, N. & Thalhamer, T. SGK1kinase regulates Th17 cells maintenance through IL-23 signalingpathway). The regulators identified are compelling new targets forregulating the Th17/Tregs balance and for switching pathogenic Th17 intonon-pathogenic ones.

Automated Procedure for Selection of Signature Genes

The invention also provides methods of determining gene signatures thatare useful in various therapeutic and/or diagnostic indications. Thegoal of these methods is to select a small signature of genes that willbe informative with respect to a process of interest. The basic conceptis that different types of information can entail different partitionsof the “space” of the entire genome (>20k genes) into subsets ofassociated genes. This strategy is designed to have the best coverage ofthese partitions, given the constraint on the signature size. Forinstance, in some embodiments of this strategy, there are two types ofinformation: (i) temporal expression profiles; and (ii) functionalannotations. The first information source partitions the genes into setsof co-expressed genes. The information source partitions the genes intosets of co-functional genes. A small set of genes is then selected suchthat there are a desired number of representatives from each set, forexample, at least 10 representatives from each co-expression set and atleast 10 representatives from each co-functional set. The problem ofworking with multiple sources of information (and thus aiming to “cover”multiple partitions) is known in the theory of computer science asSet-Cover. While this problem cannot be solved to optimality (due to itsNP-hardness) it can be approximated to within a small factor. In someembodiments, the desired number of representatives from each set is oneor more, at least 2, 5 or more, 10 or more, 15 or more, 20 or more, 25or more, 30 or more, 35 or more, 40 or more, 50 or more, 60 or more, 70or more, 80 or more, 90 or more, or 100 or more.

An important feature of this approach is that it can be given either thesize of the signature (and then find the best coverage it can under thisconstraint); or the desired level of coverage (and then select theminimal signature size that can satisfy the coverage demand).

An exemplary embodiment of this procedure is the selection of the275-gene signature (Table 1), which combined several criteria to reflectas many aspect of the differentiation program as was possible. Thefollowing requirements were defined: (1) the signature must include allof the TFs that belong to a Th17 microarray signature (comparing toother CD4+ T cells, see e.g., Wei et al., in Immunity vol. 30 155-167(2009)), see Methods described herein); that are included as regulatorsin the network and are at least slightly differentially expressed; orthat are strongly differentially expressed; (2) it must include at least10 representatives from each cluster of genes that have similarexpression profiles; (3) it must contain at least 5 representatives fromthe predicted targets of each TF in the different networks; (4) it mustinclude a minimal number of representatives from each enriched GeneOntology (GO) category (computed over differentially expressed genes);and, (5) it must include a manually assembled list of ˜100 genes thatare related to the differentiation process, including the differentiallyexpressed cytokines, receptor molecules and other cell surfacemolecules. Since these different criteria might generate substantialoverlaps, a set-cover algorithm was used to find the smallest subset ofgenes that satisfies all of five conditions. 18 genes whose expressionshowed no change (in time or between treatments) in the microarray datawere added to this list.

Use of Signature Genes

The invention provides T cell related gene signatures for use in avariety of diagnostic and/or therapeutic indications. For example, theinvention provides Th17 related signatures that are useful in a varietyof diagnostic and/or therapeutic indications. “Signatures” in thecontext of the present invention encompasses, without limitation nucleicacids, together with their polymorphisms, mutations, variants,modifications, subunits, fragments, and other analytes or sample-derivedmeasures.

Exemplary signatures are shown in Tables 1 and 2 and are collectivelyreferred to herein as, inter alia, “Th17-associated genes,”“Th17-associated nucleic acids,” “signature genes,” or “signaturenucleic acids.”

These signatures are useful in methods of diagnosing, prognosing and/orstaging an immune response in a subject by detecting a first level ofexpression, activity and/or function of one or more signature genes orone or more products of one or more signature genes selected from thoselisted in Table 1 or Table 2 and comparing the detected level to acontrol of level of signature gene or gene product expression, activityand/or function, wherein a difference in the detected level and thecontrol level indicates that the presence of an immune response in thesubject.

These signatures are useful in methods of monitoring an immune responsein a subject by detecting a level of expression, activity and/orfunction of one or more signature genes or one or more products of oneor more signature genes selected from those listed in Table 1 or Table 2at a first time point, detecting a level of expression, activity and/orfunction of one or more signature genes or one or more products of oneor more signature genes selected from those listed in Table 1 or Table 2at a second time point, and comparing the first detected level ofexpression, activity and/or function with the second detected level ofexpression, activity and/or function, wherein a change in the first andsecond detected levels indicates a change in the immune response in thesubject.

These signatures are useful in methods of identifying patientpopulations at risk or suffering from an immune response based on adetected level of expression, activity and/or function of one or moresignature genes or one or more products of one or more signature genesselected from those listed in Table 1 or Table 2. These signatures arealso useful in monitoring subjects undergoing treatments and therapiesfor aberrant immune response(s) to determine efficaciousness of thetreatment or therapy. These signatures are also useful in monitoringsubjects undergoing treatments and therapies for aberrant immuneresponse(s) to determine whether the patient is responsive to thetreatment or therapy. These signatures are also useful for selecting ormodifying therapies and treatments that would be efficacious intreating, delaying the progression of or otherwise ameliorating asymptom of an aberrant immune response. The signatures provided hereinare useful for selecting a group of patients at a specific state of adisease with accuracy that facilitates selection of treatments.

The present invention also comprises a kit with a detection reagent thatbinds to one or more signature nucleic acids. Also provided by theinvention is an array of detection reagents, e.g., oligonucleotides thatcan bind to one or more signature nucleic acids. Suitable detectionreagents include nucleic acids that specifically identify one or moresignature nucleic acids by having homologous nucleic acid sequences,such as oligonucleotide sequences, complementary to a portion of thesignature nucleic acids packaged together in the form of a kit. Theoligonucleotides can be fragments of the signature genes. For examplethe oligonucleotides can be 200, 150, 100, 50, 25, 10 or fewernucleotides in length. The kit may contain in separate container orpackaged separately with reagents for binding them to the matrix),control formulations (positive and/or negative), and/or a detectablelabel such as fluorescein, green fluorescent protein, rhodamine, cyaninedyes, Alexa dyes, luciferase, radiolabels, among others. Instructions(e.g., written, tape, VCR, CD-ROM, etc.) for carrying out the assay maybe included in the kit. The assay may for example be in the form of aNorthern hybridization or DNA chips or a sandwich ELISA or any othermethod as known in the art. Alternatively, the kit contains a nucleicacid substrate array comprising one or more nucleic acid sequences.

Use of T Cell Modulating Agents

Suitable T cell modulating agent(s) for use in any of the compositionsand methods provided herein include an antibody, a soluble polypeptide,a polypeptide agent, a peptide agent, a nucleic acid agent, a nucleicacid ligand, or a small molecule agent. By way of non-limiting example,suitable T cell modulating agents or agents for use in combination withone or more T cell modulating agents are shown below in Table 10.

TABLE 10 T cell Modulating Agents Target Agent CCR6 prostaglandin E2,lipopolysaccharide, mip-3alpha, vegf, rantes, calcium, bortezomib, ccl4,larc, tarc, lipid, E. coli B5 lipopolysaccharide CCR5 cholesterol,cyclosporin a, glutamine, methionine, guanine, simvastatin, threonine,indinavir, lipoxin A4, cysteine, prostaglandin E2, zinc, dapta,17-alpha-ethinylestradiol, polyacrylamide, progesterone, zidovudine,rapamycin, rantes, glutamate, alanine, valine, ccl4, quinine, NSC651016, methadone, pyrrolidine dithiocarbamate, palmitate,nor-binaltorphimine, interferon beta-1a, vitamin-e, tak779,lipopolysaccharide, cisplatin, albuterol, fluvoxamine, vicriviroc,bevirimat, carbon tetrachloride, galactosylceramide, ATP-gamma-S,cytochalasin d, hemozoin, CP 96345, tyrosine, etravirine, vitamin d, mip1alpha, ammonium, tyrosine sulfate, isoleucine, isopentenyl diphosphate,Il 10, serine, N-acetyl-L- cysteine, histamine, cocaine, ritonavir,tipranavir, aspartate, atazanavir, tretinoin, ATP, ribavirin, butyrate,N-nitro-L-arginine methyl ester, larc, buthionine sulfoximine, DAPTA,aminooxypentane-rantes, triamcinolone acetonide, shikonin, actinomycind, bucladesine, aplaviroc, nevirapine, N-formyl-Met-Leu-Phe, cyclosporinA, lipoarabinomannan, nucleoside, sirolimus, morphine, mannose, calcium,heparin, c-d4i, pge2, beta- estradiol, mdms, dextran sulfate,dexamethasone, arginine, ivig, mcp 2, cyclic amp, U 50488H,N-methyl-D-aspartate, hydrogen peroxide, 8- carboxamidocyclazocine,latex, groalpha, xanthine, ccl3, retinoic acid, Maraviroc, sdf 1,opiate, efavirenz, estrogen, bicyclam, enfuvirtide, filipin, bleomycin,polysaccharide, tarc, pentoxifylline, E. coli B5 lipopolysaccharide,methylcellulose, maraviroc ITGA3 SP600125, paclitaxel, decitabine,e7820, retinoid, U0126, serine, retinoic acid, tyrosine, forskolin, Ca2+IRF4 prostaglandin E2, phorbol myristate acetate, lipopolysaccharide,A23187, tacrolimus, trichostatin A, stallimycin, imatinib, cyclosporinA, tretinoin, bromodeoxyuridine, ATP-gamma-S, ionomycin BATF Cyclic AMP,serine, tacrolimus, beta-estradiol, cyclosporin A, leucine RBPJ zinc,tretinoin PROCR lipopolysaccharide, cisplatin, fibrinogen,1,10-phenanthroline, 5-N- ethylcarboxamido adenosine, cystathionine,hirudin, phospholipid, Drotrecogin alfa, vegf, Phosphatidylethanolamine,serine, gamma- carboxyglutamic acid, calcium, warfarin, endotoxin,curcumin, lipid, nitric oxide ZEB1 resveratrol, zinc, sulforafan,sorafenib, progesterone, PD-0332991, dihydrotestosterone, silibinin,LY294002, 4-hydroxytamoxifen, valproic acid, beta-estradiol, forskolin,losartan potassium, fulvestrant, vitamin d POU2AF1 terbutaline, phorbolmyristate acetate, bucladesine, tyrosine, ionomycin, KT5720, H89 EGR1ghrelin, ly294002, silicone, sodium, propofol, 1,25 dihydroxy vitamind3, tetrodotoxin, threonine, cyclopiazonic acid, urea, quercetin,ionomycin, 12-o-tetradecanoylphorbol 13-acetate, fulvestrant,phenylephrine, formaldehyde, cysteine, leukotriene C4, prazosin,LY379196, vegf, rapamycin, leupeptin, pd 98, 059, ruboxistaurin, pCPT-cAMP, methamphetamine, nitroprusside, H-7, Ro31-8220, phosphoinositide,lysophosphatidylcholine, bufalin, calcitriol, leuprolide,isobutylmethylxanthine, potassium chloride, acetic acid, cyclothiazide,quinolinic acid, tyrosine, adenylate, resveratrol, topotecan, genistein,thymidine, D-glucose, mifepristone, lysophosphatidic acid, leukotrieneD4, carbon monoxide, poly rI:rC-RNA, sp 600125, agar, cocaine, 4-nitroquinoline-1-oxide, tamoxifen, lead, fibrinogen, tretinoin,atropine, mithramycin, K+, epigallocatechin-gallate,ethylenediaminetetraacetic acid, h2o2, carbachol,sphingosine-1-phosphate, iron, 5- hydroxytryptamine, amphetamine,SP600125, actinomycin d, SB203580, cyclosporin A, norepinephrine,okadaic acid, ornithine, LY294002, pge2, beta-estradiol, glucose,erlotinib, arginine, 1-alpha, 25-dihydroxy vitamin D3, dexamethasone,pranlukast, phorbol myristate acetate, nimodipine, desipramine, cyclicamp, N-methyl-D-aspartate, atipamezole, acadesine, losartan, salvin,methylnitronitrosoguanidine, EGTA, gf 109203x, nitroarginine,5-N-ethylcarboxamido adenosine, 15-deoxy-delta-12, 14- PGJ 2, dbc-amp,manganese superoxide, di(2-ethylhexyl) phthalate, egcg, mitomycin C,6,7-dinitroquinoxaline-2, 3-dione, GnRH-A, estrogen, ribonucleic acid,imipramine, bapta, L-triiodothyronine, prostaglandin, forskolin,nogalamycin, losartan potassium, lipid, vincristine,2-amino-3-phosphonopropionic acid, prostacyclin, methylnitrosourea,cyclosporin a, vitamin K3, thyroid hormone, diethylstilbestrol,D-tubocurarine, tunicamycin, caffeine, phorbol, guanine,bisindolylmaleimide, apomorphine, arachidonic acid, SU6656,prostaglandin E2, zinc, ptx1, progesterone, cyclosporin H,phosphatidylinositol, U0126, hydroxyapatite, epoprostenol, glutamate,5fluorouracil, indomethacin, 5-fluorouracil, RP 73401, Ca2+, superoxide,trifluoperazine, nitric oxide, lipopolysaccharide, cisplatin, diazoxide,tgf beta1, calmidazolium, anisomycin, paclitaxel, sulindac sulfide,ganciclovir, gemcitabine, testosterone, ag 1478, glutamyl-Se-methylselenocysteine, doxorubicin, tolbutamide, cytochalasin d, PD98059,leucine, SR 144528, cyclic AMP, matrigel, haloperidol, serine, sb203580, triiodothyronine, reverse, N-acetyl-L-cysteine, ethanol, s-nitroso-n-acetylpenicillamine, curcumin, l-nmma, H89, tpck, calyculin a,chloramphenicol, A23187, dopamine, platelet activating factor, arsenite,selenomethylselenocysteine, ropinirole, saralasin, methylphenidate,gentamicin, reserpine, triamcinolone acetonide, methyl methanesulfonate,wortmannin, thapsigargin, deferoxamine, calyculin A, peptidoglycan,dihydrotestosterone, calcium, phorbol-12-myristate, ceramide, nmda,6-cyano-7-nitroquinoxaline-2,3-dione, hydrogen peroxide, carrageenan,sch 23390, linsidomine, oxygen, clonidine, fluoxetine, retinoid,troglitazone, retinoic acid, epinephrine, n acetylcysteine, KN-62,carbamylcholine, 2-amino-5-phosphonovaleric acid, oligonucleotide, gnrh,rasagiline, 8-bromo-cAMP, muscarine, tacrolimus, kainic acid,chelerythrine, inositol 1,4,5 trisphosphate, yohimbine, acetylcholine,atp, 15-deoxy-delta-12, 14-prostaglandin j2, ryanodine, CpGoligonucleotide, cycloheximide, BAPTA-AM, phenylalanine ETV6lipopolysaccharide, retinoic acid, prednisolone, valproic acid,tyrosine, cerivastatin, vegf, agar, imatinib, tretinoin IL17RA rantes,lipopolysaccharide, 17-alpha-ethinylestradiol, camptothecin, E. coli B5lipopolysaccharide EGR2 phorbol myristate acetate, lipopolysaccharide,platelet activating factor, carrageenan, edratide, 5-N-ethylcarboxamidoadenosine, potassium chloride, dbc-amp, tyrosine, PD98059, camptothecin,formaldehyde, prostaglandin E2, leukotriene C4, zinc, cyclic AMP,GnRH-A, bucladesine, thapsigargin, kainic acid, cyclosporin A,mifepristone, leukotriene D4, LY294002, L-triiodothyronine, calcium,beta-estradiol, H89, dexamethasone, cocaine SP4 betulinic acid, zinc,phorbol myristate acetate, LY294002, methyl 2- cyano-3,12-dioxoolean-1,9-dien-28-oate, beta-estradiol, Ca2+ IRF8 oligonucleotide,chloramphenicol, lipopolysaccharide, estrogen, wortmannin, pirinixicacid, carbon monoxide, retinoic acid, tyrosine NFKB1 Bay 11-7085,Luteolin, Triflusal, Bay 11-7821, Thalidomide, Caffeic acid phenethylester, Pranlukast TSC22D3 phorbol myristate acetate, prednisolone,sodium, dsip, tretinoin, 3- deazaneplanocin, gaba, PD98059, leucine,triamcinolone acetonide, prostaglandin E2, steroid, norepinephrine,U0126, acth, calcium, ethanol, beta-estradiol, lipid, chloropromazine,arginine, dexamethasone PML lipopolysaccharide, glutamine, thyroidhormone, cadmium, lysine, tretinoin, bromodeoxyuridine, etoposide,retinoid, pic 1, arsenite, arsenic trioxide, butyrate, retinoic acid,alpha-retinoic acid, h2o2, camptothecin, cysteine, leucine, zinc,actinomycin d, proline, stallimycin, U0126 IL12RB1 prostaglandin E2,phorbol myristate acetate, lipopolysaccharide, bucladesine,8-bromo-cAMP, gp 130, AGN194204, galactosylceramide- alpha, tyrosine,ionomycin, dexamethasone, Il-12 IL21R azathioprine, lipopolysaccharide,okadaic acid, E. coli B5 lipopolysaccharide, calyculin A NOTCH1interferon beta-1a, lipopolysaccharide, cisplatin, tretinoin, oxygen,vitamin B12, epigallocatechin-gallate, isobutylmethylxanthine,threonine, apomorphine, matrigel, trichostatin A, vegf,2-acetylaminofluorene, rapamycin, dihydrotestosterone, poly rI:rC-RNA,hesperetin, valproic acid, asparagine, lipid, curcumin, dexamethasone,glycogen, CpG oligonucleotide, nitric oxide ETS2 oligonucleotide MINAphorbol myristate acetate, 4-hydroxytamoxifen SMARCA4 cyclic amp,cadmium, lysine, tretinoin, latex, androstane, testosterone, sucrose,tyrosine, cysteine, zinc, oligonucleotide, estrogen, steroid,trichostatin A, tpmp, progesterone, histidine, atp, trypsinogen,glucose, agar, lipid, arginine, vancomycin, dihydrofolate FAS hoechst33342, ly294002, 2-chlorodeoxyadenosine, glutamine, cd 437,tetrodotoxin, cyclopiazonic acid, arsenic trioxide, phosphatidylserine,niflumic acid, gliadin, ionomycin, safrole oxide, methotrexate,rubitecan, cysteine, propentofylline, vegf, boswellic acids, rapamycin,pd 98, 059, captopril, methamphetamine, vesnarinone, tetrapeptide,oridonin, raltitrexed, pirinixic acid, nitroprusside, H-7,beta-boswellic acid, adriamycin, concanamycin a, etoposide, trastuzumab,cyclophosphamide, ifn-alpha, tyrosine, rituximab, selenodiglutathione,chitosan, omega-N- methylarginine, creatinine, resveratrol, topotecan,genistein, trichostatin A, decitabine, thymidine, D-glucose,mifepristone, tetracycline, Sn50 peptide, poly rI:rC-RNA, actinomycin D,sp 600125, doxifluridine, agar, ascorbic acid, acetaminophen, aspirin,tamoxifen, okt3, edelfosine, sulforafan, aspartate, antide, n,n-dimethylsphingosine, epigallocatechin- gallate, N-nitro-L-argininemethyl ester, h2o2, cerulenin, sphingosine-1- phosphate, SP600125,sodium nitroprusside, glycochenodeoxycholic acid, ceramides, actinomycind, SB203580, cyclosporin A, morphine, LY294002, n(g)-nitro-l-argininemethyl ester, 4-hydroxynonenal, piceatannol, valproic acid,beta-estradiol, 1-alpha, 25-dihydroxy vitamin D3, arginine,dexamethasone, sulfadoxine, phorbol myristate acetate, beta-lapachone,nitrofurantoin, chlorambucil, methylnitronitrosoguanidine, CD 437,opiate, egcg, mitomycin C, estrogen, ribonucleic acid, fontolizumab,tanshinone iia, recombinant human endostatin, fluoride,L-triiodothyronine, bleomycin, forskolin, nonylphenol, zymosan A,vincristine, daunorubicin, prednisolone, cyclosporin a, vitamin K3,diethylstilbestrol, deoxyribonucleotide, suberoylanilide hydroxamicacid, orlistat, 3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyltetrazoliumbromide, rottlerin, arachidonic acid, ibuprofen, prostaglandin E2,toremifene, depsipeptide, ochratoxin A, (glc)4, phosphatidylinositol,mitomycin c, rantes, sphingosine, indomethacin, 5fluorouracil,phosphatidylcholine, 5-fluorouracil, mg 132, thymidylate,trans-cinnamaldehyde, sterol, polyadenosine diphosphate ribose, nitricoxide, vitamin e succinate, lipopolysaccharide, cisplatin, herbimycin a,5- aza-2′deoxycytidine, proteasome inhibitor PSI, 2,5-hexanedione,epothilone B, caffeic acid phenethyl ester, glycerol 3-phosphate, tgfbeta1, anisomycin, paclitaxel, gemcitabine, medroxyprogesterone acetate,hymecromone, testosterone, ag 1478, doxombicin, S-nitroso-N-acetylpenicillamine, adpribose, sulforaphane, vitamin d, annexin-v,lactate, reactive oxygen species, sb 203580, serine,N-acetyl-L-cysteine, dutp, infliximab, ethanol, curcumin, cytarabine,tpck, calyculin a, dopamine, gp 130, bromocriptine, apicidin, fattyacid, citrate, glucocorticoid, arsenite, butyrate, peplomycin,oxaliplatin, camptothecin, benzyloxycarbonyl-Leu-Leu-Leu aldehyde,clofibrate, carbon, wortmannin, fludarabine,N-(3-(aminomethyl)benzyl)acetamidine, sirolimus, peptidoglycan,c2ceramide, dihydrotestosterone, 7- aminoactinomycin d, carmustine,heparin, ceramide, paraffin, mitoxantrone, docosahexaenoic acid, vitamina, ivig, hydrogen peroxide, 7-ethyl-10-hydroxy-camptomecin, oxygen,pydrin, bortezomib, retinoic acid,1,4-phenylenebis(methylene)selenocyanate, teriflunomide, epinephrine, nacetylcysteine, noxa, irinotecan, oligonucleotide, d-api, rasagiline,8-bromo-cAMP, atpo, agarose, fansidar, clobetasol propionate,teniposide, aurintricarboxylic acid, polysaccharide, CpGoligonucleotide, cycloheximide IRF1 tamoxifen, chloramphenicol,polyinosinic-polycytidylic acid, inosine monophosphate, suberoylanilidehydroxamic acid, butyrate, iron, gliadin, zinc, actinomycin d,deferoxamine, phosphatidylinositol, adenine, ornthine, rantes, calcium,2′,5′-oligoadenylate, pge2, poly(i-c), indoleamine, arginine, estradiol,nitric oxide, etoposide, adriamycin, oxygen, retinoid, guanylate,troglitazone, ifn-alpha, retinoic acid, tyrosine, adenylate, am 580,guanosine, oligonucleotide, estrogen, thymidine, tetracycline, serine,sb 203580, pdtc, lipid, cycloheximide MYC cd 437, 1,25 dihydroxy vitamind3, phenethyl isothiocyanate, threonine, arsenic trioxide, salicylicacid, quercetin, prostaglandin E1, ionomycin, 12-o-tetradecanoylphorbol13-acetate, fulvestrant, phenylephrine, fisetin, 4-coumaric acid,dihydroartemisinin, 3-deazaadenosine, nitroprusside, pregna-4,17-diene-3, 16-dione, adriamycin, bromodeoxyuridine, AGN194204,STA-9090, isobutylmethylxanthine, potassium chloride, docetaxel,quinolinic acid, 5,6,7,8-tetrahydrobiopterin, propranolol, delta 7-pga1,topotecan, AVI-4126, trichostatin A, decitabine, thymidine, D-glucose,mifepristone, poly rI:rC-RNA, letrozole, L-threonine, 5-hydroxytryptamine, bucladesine, SB203580, 1′-acetoxychavicol acetate,cyclosporin A, okadaic acid, dfmo, LY294002, hmba, piceatannol, 2′,5′-oligoadenylate, 4-hydroxytamoxifen, butylbenzyl phthalate,dexamethasone, ec 109, phosphatidic acid, grape seed extract, phorbolmyristate acetate, coumermycin, tosylphenylalanyl chloromethyl ketone,CD 437, di(2-ethylhexyl) phthalate, butyrine, cytidine, sodium arsenite,tanshinone iia, L-triiodothyronine, niacinamide, glycogen, daunorubicin,vincristine, carvedilol, bizelesin, 3-deazaneplanocin, phorbol,neplanocin a, panobinostat, [alcl], phosphatidylinositol, U0126,dichlororibofuranosylbenzimidazole, flavopiridol, 5-fluorouracil,verapamil, cyclopamine, nitric oxide, cisplatin, hrgbeta1,5,6-dichloro-1- beta-d-ribofuranosylbenzimidazole, amsacrine,gemcitabine, aristeromycin, medroxyprogesterone acetate, gambogic acid,leucine, alpha-naphthyl acetate, cyclic AMP, reactive oxygen species, PD180970, curcumin, chloramphenicol, A23187, crocidolite asbestos, 6-hydroxydopamine, cb 33, arsenite, gentamicin, benzyloxycarbonyl-Leu-Leu-Leu aldehyde, clofibrate, wortmannin, sirolimus, ceramide,melphalan, 3M-001, linsidomine, CP-55940, hyaluronic acid, ethionine,clonidine, retinoid, bortezomib, oligonucleotide, methyl 2-cyano-3, 12-dioxoolean-1, 9-dien-28-oate, tacrolimus, embelin, methyl-beta-cyclodextrin, 3M-011, folate, ly294002, PP1, hydroxyurea, aclarubicin,phenylbutyrate, PD 0325901, methotrexate, Cd2+, prazosin, vegf,rapamycin, alanine, phenobarbital, pd 98, 059, trapoxin, 4-hydroperoxycyclophosphamide, methamphetamine, s-(1,2-dichlorovinyl)-l-cysteine, aphidicolin, vesnarinone, ADI PEG20,pirinixic acid, wp631, H-7, carbon tetrachloride, bufalin, 2,2-dimethylbutyric acid, etoposide, calcitriol, trastuzumab,cyclophosphamide, harringtonine, tyrosine, N(6)-(3-iodobenzyl)-5′-N-methylcarboxamidoadenosine, resveratrol, thioguanine, genistein, S-nitroso-N-acetyl-DL-penicillamine, zearalenone, lysophosphatidic acid,Sn50 peptide, roscovitine, actinomycin D, propanil, agar, tamoxifen,acetaminophen, imatinib, tretinoin, mithramycin, ATP, epigallocatechin-gallate, ferric ammonium citrate, acyclic retinoid, L-cysteine,nitroblue tetrazolium, actinomycin d, sodium nitroprusside, 1,2-dimethylhydrazine, dibutyl phthalate, ornithine, 4-hydroxynonenal, beta-estradiol, 1-alpha, 25-dihydroxy vitamin D3, cyproterone acetate,nimodipine, nitrofurantoin, temsirolimus, 15-deoxy-delta-12, 14-PGJ 2,estrogen, ribonucleic acid, ciprofibrate, alpha-amanitin, SB 216763,bleomycin, forskolin, prednisolone, cyclosporin a, thyroid hormone,tunicamycin, phosphorothioate, suberoylanilide hydroxamic acid, pga2,3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide, benzamideriboside, bisindolylmaleimide, SU6656, prostaglandin E2, depsipeptide,zidovudine, cerivastatin, progesterone, sethoxydim, indomethacin, mg132, mezerein, pyrrolidine dithiocarbamate, vitamin e succinate,herbimycin a, 5-aza-2′deoxycytidine, lipopolysaccharide, diazoxide,anisomycin, paclitaxel, sodium dodecylsulfate, nilotinib, oxysterol,doxombicin, lipofectamine, PD98059, steroid, delta-12-pgj2, serine, H-8,N-acetyl-L-cysteine, ethanol, n-(4-hydroxyphenyl)retinamide, tiazofurin,cytarabine, H89, 10-hydroxycamptothecin, everolimus, lactacystin, n(1),n(12)-bis(ethyl)spermine, silibinin, glucocorticoid, butyrate,camptothecin, triamcinolone acetonide, tocotrienol, n-ethylmaleimide,phorbol 12, 13-didecanoate, thapsigargin, deferoxamine, R59949,bryostatin 1, paraffin, romidepsin, vitamin a, docosahexaenoic acid,hydrogen peroxide, droloxifene, saikosaponin, fluoxetine, retinoic acid,n acetylcysteine, dithiothreitol, cordycepin, agarose, 8-bromo-cAMP, D-galactosamine, tachyplesin i, theophylline, metoprolol, SU6657, 15-deoxy-delta-12, 14-prostaglandin j2, dmso, 2-amino-5-azotoluene,cycloheximide

It will be appreciated that administration of therapeutic entities inaccordance with the invention will be administered with suitablecarriers, excipients, and other agents that are incorporated intoformulations to provide improved transfer, delivery, tolerance, and thelike. A multitude of appropriate formulations can be found in theformulary known to all pharmaceutical chemists: Remington'sPharmaceutical Sciences (15th ed, Mack Publishing Company, Easton, Pa.(1975)), particularly Chapter 87 by Blaug, Seymour, therein. Theseformulations include, for example, powders, pastes, ointments, jellies,waxes, oils, lipids, lipid (cationic or anionic) containing vesicles(such as Lipofectin™), DNA conjugates, anhydrous absorption pastes,oil-in-water and water-in-oil emulsions, emulsions carbowax(polyethylene glycols of various molecular weights), semi-solid gels,and semi-solid mixtures containing carbowax. Any of the foregoingmixtures may be appropriate in treatments and therapies in accordancewith the present invention, provided that the active ingredient in theformulation is not inactivated by the formulation and the formulation isphysiologically compatible and tolerable with the route ofadministration. See also Baldrick P. “Pharmaceutical excipientdevelopment: the need for preclinical guidance.” Regul. ToxicolPharmacol. 32(2):210-8 (2000), Wang W. “Lyophilization and developmentof solid protein pharmaceuticals.” Int. J. Pharm. 203(1-2):1-60 (2000),Charman W N “Lipids, lipophilic drugs, and oral drug delivery-someemerging concepts.” J Pharm Sci. 89(8):967-78 (2000), Powell et al.“Compendium of excipients for parenteral formulations” PDA J Pharm SciTechnol. 52:238-311 (1998) and the citations therein for additionalinformation related to formulations, excipients and carriers well knownto pharmaceutical chemists.

Therapeutic formulations of the invention, which include a T cellmodulating agent, are used to treat or alleviate a symptom associatedwith an immune-related disorder or an aberrant immune response. Thepresent invention also provides methods of treating or alleviating asymptom associated with an immune-related disorder or an aberrant immuneresponse. A therapeutic regimen is carried out by identifying a subject,e.g., a human patient suffering from (or at risk of developing) animmune-related disorder or aberrant immune response, using standardmethods. For example, T cell modulating agents are useful therapeutictools in the treatment of autoimmune diseases and/or inflammatorydisorders. In certain embodiments, the use of T cell modulating agentsthat modulate, e.g., inhibit, neutralize, or interfere with, Th17 T celldifferentiation is contemplated for treating autoimmune diseases and/orinflammatory disorders. In certain embodiments, the use of T cellmodulating agents that modulate, e.g., enhance or promote, Th17 T celldifferentiation is contemplated for augmenting Th17 responses, forexample, against certain pathogens and other infectious diseases. The Tcell modulating agents are also useful therapeutic tools in varioustransplant indications, for example, to prevent, delay or otherwisemitigate transplant rejection and/or prolong survival of a transplant,as it has also been shown that in some cases of transplant rejection,Th17 cells might also play an important role. (See e.g., Abadja F,Sarraj B, Ansari M J., “Significance of T helper 17 immunity intransplantation.” Curr Opin Organ Transplant. 2012 February; 17(1):8-14.doi: 10.1097/MOT.0b013e32834ef4e4). The T cell modulating agents arealso useful therapeutic tools in cancers and/or anti-tumor immunity, asTh17/Treg balance has also been implicated in these indications. Forexample, some studies have suggested that IL-23 and Th17 cells play arole in some cancers, such as, by way of non-limiting example,colorectal cancers. (See e.g., Ye J, Livergood R S, Peng G. “The roleand regulation of human Th17 cells in tumor immunity.” Am J Pathol. 2013January; 182(1):10-20. doi: 10.1016/j.ajpath.2012.08.041. Epub 2012 Nov.14). The T cell modulating agents are also useful in patients who havegenetic defects that exhibit aberrant Th17 cell production, for example,patients that do not produce Th17 cells naturally.

The T cell modulating agents are also useful in vaccines and/or asvaccine adjuvants against autoimmune disorders, inflammatory diseases,etc. The combination of adjuvants for treatment of these types ofdisorders are suitable for use in combination with a wide variety ofantigens from targeted self-antigens, i.e., autoantigens, involved inautoimmunity, e.g., myelin basic protein; inflammatory self-antigens,e.g., amyloid peptide protein, or transplant antigens, e.g.,alloantigens. The antigen may comprise peptides or polypeptides derivedfrom proteins, as well as fragments of any of the following:saccharides, proteins, polynucleotides or oligonucleotides,autoantigens, amyloid peptide protein, transplant antigens, allergens,or other macromolecular components. In some instances, more than oneantigen is included in the antigenic composition.

Autoimmune diseases include, for example, Acquired ImmunodeficiencySyndrome (AIDS, which is a viral disease with an autoimmune component),alopecia areata, ankylosing spondylitis, antiphospholipid syndrome,autoimmune Addison's disease, autoimmune hemolytic anemia, autoimmunehepatitis, autoimmune inner ear disease (AIED), autoimmunelymphoproliferative syndrome (ALPS), autoimmune thrombocytopenic purpura(ATP), Behcet's disease, cardiomyopathy, celiac sprue-dermatitishepetiformis; chronic fatigue immune dysfunction syndrome (CFIDS),chronic inflammatory demyelinating polyneuropathy (CIPD), cicatricialpemphigold, cold agglutinin disease, crest syndrome, Crohn's disease,Degos' disease, dermatomyositis-juvenile, discoid lupus, essential mixedcryoglobulinemia, fibromyalgia-fibromyositis, Graves' disease,Guillain-Barré syndrome, Hashimoto's thyroiditis, idiopathic pulmonaryfibrosis, idiopathic thrombocytopenia purpura (ITP), IgA nephropathy,insulin-dependent diabetes mellitus, juvenile chronic arthritis (Still'sdisease), juvenile rheumatoid arthritis, Ménière's disease, mixedconnective tissue disease, multiple sclerosis, myasthenia gravis,pernacious anemia, polyarteritis nodosa, polychondritis, polyglandularsyndromes, polymyalgia rheumatica, polymyositis and dermatomyositis,primary agammaglobulinemia, primary biliary cirrhosis, psoriasis,psoriatic arthritis, Raynaud's phenomena, Reiter's syndrome, rheumaticfever, rheumatoid arthritis, sarcoidosis, scleroderma (progressivesystemic sclerosis (PSS), also known as systemic sclerosis (SS)),Sjögren's syndrome, stiffman syndrome, systemic lupus erythematosus,Takayasu arteritis, temporal arteritis/giant cell arteritis, ulcerativecolitis, uveitis, vitiligo and Wegener's granulomatosis.

In some embodiments, T cell modulating agents are useful in treating,delaying the progression of, or otherwise ameliorating a symptom of anautoimmune disease having an inflammatory component such as an aberrantinflammatory response in a subject. In some embodiments, T cellmodulating agents are useful in treating an autoimmune disease that isknown to be associated with an aberrant Th17 response, e.g., aberrantIL-17 production, such as, for example, multiple sclerosis (MS),psoriasis, inflammatory bowel disease, ulcerative colitis, Crohn'sdisease, uveitis, lupus, ankylosing spondylitis, and rheumatoidarthritis.

Inflammatory disorders include, for example, chronic and acuteinflammatory disorders. Examples of inflammatory disorders includeAlzheimer's disease, asthma, atopic allergy, allergy, atherosclerosis,bronchial asthma, eczema, glomerulonephritis, graft vs. host disease,hemolytic anemias, osteoarthritis, sepsis, stroke, transplantation oftissue and organs, vasculitis, diabetic retinopathy and ventilatorinduced lung injury.

Symptoms associated with these immune-related disorders include, forexample, inflammation, fever, general malaise, fever, pain, oftenlocalized to the inflamed area, rapid pulse rate, joint pain or aches(arthralgia), rapid breathing or other abnormal breathing patterns,chills, confusion, disorientation, agitation, dizziness, cough, dyspnea,pulmonary infections, cardiac failure, respiratory failure, edema,weight gain, mucopurulent relapses, cachexia, wheezing, headache, andabdominal symptoms such as, for example, abdominal pain, diarrhea orconstipation.

Efficaciousness of treatment is determined in association with any knownmethod for diagnosing or treating the particular immune-relateddisorder. Alleviation of one or more symptoms of the immune-relateddisorder indicates that the T cell modulating agent confers a clinicalbenefit.

Administration of a T cell modulating agent to a patient suffering froman immune-related disorder or aberrant immune response is consideredsuccessful if any of a variety of laboratory or clinical objectives isachieved. For example, administration of a T cell modulating agent to apatient is considered successful if one or more of the symptomsassociated with the immune-related disorder or aberrant immune responseis alleviated, reduced, inhibited or does not progress to a further,i.e., worse, state. Administration of T cell modulating agent to apatient is considered successful if the immune-related disorder oraberrant immune response enters remission or does not progress to afurther, i.e., worse, state.

A therapeutically effective amount of a T cell modulating agent relatesgenerally to the amount needed to achieve a therapeutic objective. Theamount required to be administered will furthermore depend on thespecificity of the T cell modulating agent for its specific target, andwill also depend on the rate at which an administered T cell modulatingagent is depleted from the free volume other subject to which it isadministered.

T cell modulating agents can be administered for the treatment of avariety of diseases and disorders in the form of pharmaceuticalcompositions. Principles and considerations involved in preparing suchcompositions, as well as guidance in the choice of components areprovided, for example, in Remington: The Science And Practice OfPharmacy 19th ed. (Alfonso R. Gennaro, et al., editors) Mack Pub. Co.,Easton, Pa.: 1995; Drug Absorption Enhancement: Concepts, Possibilities,Limitations, And Trends, Harwood Academic Publishers, Langhorne, Pa.,1994; and Peptide And Protein Drug Delivery (Advances In ParenteralSciences, Vol. 4), 1991, M. Dekker, New York.

Where polypeptide-based T cell modulating agents are used, the smallestfragment that specifically binds to the target and retains therapeuticfunction is preferred. Such fragments can be synthesized chemicallyand/or produced by recombinant DNA technology. (See, e.g., Marasco etal., Proc. Natl. Acad. Sci. USA, 90: 7889-7893 (1993)). The formulationcan also contain more than one active compound as necessary for theparticular indication being treated, preferably those with complementaryactivities that do not adversely affect each other. Alternatively, or inaddition, the composition can comprise an agent that enhances itsfunction, such as, for example, a cytotoxic agent, cytokine,chemotherapeutic agent, or growth-inhibitory agent. Such molecules aresuitably present in combination in amounts that are effective for thepurpose intended.

The invention comprehends a treatment method or Drug Discovery method ormethod of formulating or preparing a treatment comprising any one of themethods or uses herein discussed.

The present invention also relates to identifying molecules,advantageously small molecules or biologics, that may be involved ininhibiting one or more of the mutations in one or more genes selectedfrom the group consisting of DEC1, PZLP, TCF4 and CD5L. The inventioncontemplates screening libraries of small molecules or biologics toidentify compounds involved in suppressing or inhibiting expression ofsomatic mutations or alter the cells phenotypically so that the cellswith mutations behave more normally in one or more of DEC1, PZLP, TCF4and CD5L.

High-throughput screening (HTS) is contemplated for identifying smallmolecules or biologics involved in suppressing or inhibiting expressionof somatic mutations in one or more of DEC1, PZLP, TCF4 and CD5L. Theflexibility of the process has allowed numerous and disparate areas ofbiology to engage with an equally diverse palate of chemistry (see,e.g., Inglese et al., Nature Chemical Biology 3, 438-441 (2007)).Diverse sets of chemical libraries, containing more than 200,000 uniquesmall molecules, as well as natural product libraries, can be screened.This includes, for example, the Prestwick library (1,120 chemicals) ofoff-patent compounds selected for structural diversity, collectivecoverage of multiple therapeutic areas, and known safety andbioavailability in humans, as well as the NINDS Custom Collection 2consisting of a 1,040 compound-library of mostly FDA-approved drugs(see, e.g., U.S. Pat. No. 8,557,746) are also contemplated.

The NIH's Molecular Libraries Probe Production Centers Network (MLPCN)offers access to thousands of small molecules—chemical compounds thatcan be used as tools to probe basic biology and advance ourunderstanding of disease. Small molecules can help researchersunderstand the intricacies of a biological pathway or be starting pointsfor novel therapeutics. The Broad Institute's Probe Development Center(BIPDeC) is part of the MLPCN and offers access to a growing library ofover 330,000 compounds for large scale screening and medicinalchemistry. Any of these compounds may be utilized for screeningcompounds involved in suppressing or inhibiting expression of somaticmutations in one or more of DEC1, PZLP, TCF4 and CD5L.

The phrase “therapeutically effective amount” as used herein refers to anontoxic but sufficient amount of a drug, agent, or compound to providea desired therapeutic effect.

As used herein “patient” refers to any human being receiving or who mayreceive medical treatment.

A “polymorphic site” refers to a polynucleotide that differs fromanother polynucleotide by one or more single nucleotide changes.

A “somatic mutation” refers to a change in the genetic structure that isnot inherited from a parent, and also not passed to offspring.

Therapy or treatment according to the invention may be performed aloneor in conjunction with another therapy, and may be provided at home, thedoctor's office, a clinic, a hospital's outpatient department, or ahospital. Treatment generally begins at a hospital so that the doctorcan observe the therapy's effects closely and make any adjustments thatare needed. The duration of the therapy depends on the age and conditionof the patient, the stage of the a cardiovascular disease, and how thepatient responds to the treatment. Additionally, a person having agreater risk of developing a cardiovascular disease (e.g., a person whois genetically predisposed) may receive prophylactic treatment toinhibit or delay symptoms of the disease.

The medicaments of the invention are prepared in a manner known to thoseskilled in the art, for example, by means of conventional dissolving,lyophilizing, mixing, granulating or confectioning processes. Methodswell known in the art for making formulations are found, for example, inRemington: The Science and Practice of Pharmacy, 20th ed., ed. A. R.Gennaro, 2000, Lippincott Williams & Wilkins, Philadelphia, andEncyclopedia of Pharmaceutical Technology, eds. J. Swarbrick and J. C.Boylan, 1988-1999, Marcel Dekker, New York.

Administration of medicaments of the invention may be by any suitablemeans that results in a compound concentration that is effective fortreating or inhibiting (e.g., by delaying) the development of acardiovascular disease. The compound is admixed with a suitable carriersubstance, e.g., a pharmaceutically acceptable excipient that preservesthe therapeutic properties of the compound with which it isadministered. One exemplary pharmaceutically acceptable excipient isphysiological saline. The suitable carrier substance is generallypresent in an amount of 1-95% by weight of the total weight of themedicament. The medicament may be provided in a dosage form that issuitable for oral, rectal, intravenous, intramuscular, subcutaneous,inhalation, nasal, topical or transdermal, vaginal, or ophthalmicadministration. Thus, the medicament may be in form of, e.g., tablets,capsules, pills, powders, granulates, suspensions, emulsions, solutions,gels including hydrogels, pastes, ointments, creams, plasters, drenches,delivery devices, suppositories, enemas, injectables, implants, sprays,or aerosols.

In order to determine the genotype of a patient according to the methodsof the present invention, it may be necessary to obtain a sample ofgenomic DNA from that patient. That sample of genomic DNA may beobtained from a sample of tissue or cells taken from that patient.

The tissue sample may comprise but is not limited to hair (includingroots), skin, buccal swabs, blood, or saliva. The tissue sample may bemarked with an identifying number or other indicia that relates thesample to the individual patient from which the sample was taken. Theidentity of the sample advantageously remains constant throughout themethods of the invention thereby guaranteeing the integrity andcontinuity of the sample during extraction and analysis. Alternatively,the indicia may be changed in a regular fashion that ensures that thedata, and any other associated data, can be related back to the patientfrom whom the data was obtained. The amount/size of sample required isknown to those skilled in the art.

Generally, the tissue sample may be placed in a container that islabeled using a numbering system bearing a code corresponding to thepatient. Accordingly, the genotype of a particular patient is easilytraceable.

In one embodiment of the invention, a sampling device and/or containermay be supplied to the physician. The sampling device advantageouslytakes a consistent and reproducible sample from individual patientswhile simultaneously avoiding any cross-contamination of tissue.Accordingly, the size and volume of sample tissues derived fromindividual patients would be consistent.

According to the present invention, a sample of DNA is obtained from thetissue sample of the patient of interest. Whatever source of cells ortissue is used, a sufficient amount of cells must be obtained to providea sufficient amount of DNA for analysis. This amount will be known orreadily determinable by those skilled in the art.

DNA is isolated from the tissue/cells by techniques known to thoseskilled in the art (see, e.g., U.S. Pat. Nos. 6,548,256 and 5,989,431,Hirota et al., Jinrui Idengaku Zasshi. September 1989; 34(3):217-23 andJohn et al., Nucleic Acids Res. Jan. 25, 1991; 19(2):408; thedisclosures of which are incorporated by reference in their entireties).For example, high molecular weight DNA may be purified from cells ortissue using proteinase K extraction and ethanol precipitation. DNA maybe extracted from a patient specimen using any other suitable methodsknown in the art.

It is an object of the present invention to determine the genotype of agiven patient of interest by analyzing the DNA from the patent, in orderto identify a patient carrying specific somatic mutations of theinvention that are associated with developing a cardiovascular disease.In particular, the kit may have primers or other DNA markers foridentifying particular mutations such as, but not limited to, one ormore genes selected from the group consisting of DEC1, PZLP, TCF4 andCD5L.

There are many methods known in the art for determining the genotype ofa patient and for identifying or analyzing whether a given DNA samplecontains a particular somatic mutation. Any method for determininggenotype can be used for determining genotypes in the present invention.Such methods include, but are not limited to, amplimer sequencing, DNAsequencing, fluorescence spectroscopy, fluorescence resonance energytransfer (or “FRET”)-based hybridization analysis, high throughputscreening, mass spectroscopy, nucleic acid hybridization, polymerasechain reaction (PCR), RFLP analysis and size chromatography (e.g.,capillary or gel chromatography), all of which are well known to one ofskill in the art.

The methods of the present invention, such as whole exome sequencing andtargeted amplicon sequencing, have commercial applications in diagnostickits for the detection of the somatic mutations in patients. A test kitaccording to the invention may comprise any of the materials necessaryfor whole exome sequencing and targeted amplicon sequencing, forexample, according to the invention. In a particular advantageousembodiment, a diagnostic for the present invention may comprise testingfor any of the genes in disclosed herein. The kit further comprisesadditional means, such as reagents, for detecting or measuring thesequences of the present invention, and also ideally a positive andnegative control.

The present invention further encompasses probes according to thepresent invention that are immobilized on a solid or flexible support,such as paper, nylon or other type of membrane, filter, chip, glassslide, microchips, microbeads, or any other such matrix, all of whichare within the scope of this invention. The probe of this form is nowcalled a “DNA chip”. These DNA chips can be used for analyzing thesomatic mutations of the present invention. The present inventionfurther encompasses arrays or microarrays of nucleic acid molecules thatare based on one or more of the sequences described herein. As usedherein “arrays” or “microarrays” refers to an array of distinctpolynucleotides or oligonucleotides synthesized on a solid or flexiblesupport, such as paper, nylon or other type of membrane, filter, chip,glass slide, or any other suitable solid support. In one embodiment, themicroarray is prepared and used according to the methods and devicesdescribed in U.S. Pat. Nos. 5,446,603; 5,545,531; 5,807,522; 5,837,832;5,874,219; 6,114,122; 6,238,910; 6,365,418; 6,410,229; 6,420,114;6,432,696; 6,475,808 and 6,489,159 and PCT Publication No. WO 01/45843A2, the disclosures of which are incorporated by reference in theirentireties.

EXAMPLES & TECHNOLOGIES AS TO THE INSTANT INVENTION

The following examples are given for the purpose of illustrating variousembodiments of the invention and are not meant to limit the presentinvention in any fashion. The present examples, along with the methodsdescribed herein are presently representative of preferred embodiments,are exemplary, and are not intended as limitations on the scope of theinvention. Changes therein and other uses which are encompassed withinthe spirit of the invention as defined by the scope of the claims willoccur to those skilled in the art.

In this regard, mention is made that mutations in cells and also mutatedmice for use in or as to the invention can be by way of the CRISPR-Cassystem or a Cas9-expressing eukaryotic cell or Cas-9 expressingeukaryote, such as a mouse. The Cas9-expressing eukaryotic cell oreukaryote, e.g., mouse, can have guide RNA delivered or administeredthereto, whereby the RNA targets a loci and induces a desired mutationfor use in or as to the invention. With respect to general informationon CRISPR-Cas Systems, components thereof, and delivery of suchcomponents, including methods, materials, delivery vehicles, vectors,particles, and making and using thereof, including as to amounts andformulations, as well as Cas9-expressing eukaryotic cells, Cas-9expressing eukaryotes, such as a mouse, all useful in or as to theinstant invention, reference is made to: U.S. Pat. Nos. 8,697,359,8,771,945, 8,795,965, 8,865,406, 8,871,445, 8,889,356, 8,889,418,8,895,308, 8,932,814, 8,945,839, 8,906,616; US Patent Publications US2014-0310830 (U.S. application Ser. No. 14/105,031), US 2014-0287938 A1(U.S. application Ser. No. 14/213,991), US 2014-0273234 A1 (U.S.application Ser. No. 14/293,674), US2014-0273232 A1 (U.S. applicationSer. No. 14/290,575), US 2014-0273231 (U.S. application Ser. No.14/259,420), US 2014-0256046 A1 (U.S. application Ser. No. 14/226,274),US 2014-0248702 A1 (U.S. application Ser. No. 14/258,458), US2014-0242700 A1 (U.S. application Ser. No. 14/222,930), US 2014-0242699A1 (U.S. application Ser. No. 14/183,512), US 2014-0242664 A1 (U.S.application Ser. No. 14/104,990), US 2014-0234972 A1 (U.S. applicationSer. No. 14/183,471), US 2014-0227787 A1 (U.S. application Ser. No.14/256,912), US 2014-0189896 A1 (U.S. application Ser. No. 14/105,035),US 2014-0186958 (U.S. application Ser. No. 14/105,017), US 2014-0186919A1 (U.S. application Ser. No. 14/104,977), US 2014-0186843 A1 (U.S.application Ser. No. 14/104,900), US 2014-0179770 A1 (U.S. applicationSer. No. 14/104,837) and US 2014-0179006 A1 (U.S. application Ser. No.14/183,486), US 2014-0170753 (U.S. application Ser. No. 14/183,429);European Patents/Patent Applications: EP 2 771 468 (EP13818570.7), EP 2764 103 (EP13824232.6), and EP 2 784 162 (EP14170383.5); and PCT PatentPublications WO 2014/093661 (PCT/US2013/074743), WO 2014/093694(PCT/US2013/074790), WO 2014/093595 (PCT/US2013/074611), WO 2014/093718(PCT/US2013/074825), WO 2014/093709 (PCT/US2013/074812), WO 2014/093622(PCT/US2013/074667), WO 2014/093635 (PCT/US2013/074691), WO 2014/093655(PCT/US2013/074736), WO 2014/093712 (PCT/US2013/074819), WO2014/093701(PCT/US2013/074800), WO2014/018423 (PCT/US2013/051418), WO 2014/204723(PCT/US2014/041790), WO 2014/204724 (PCT/US2014/041800), WO 2014/204725(PCT/US2014/041803), WO 2014/204726 (PCT/US2014/041804), WO 2014/204727(PCT/US2014/041806), WO 2014/204728 (PCT/US2014/041808), WO 2014/204729(PCT/US2014/041809), and:

-   Multiplex genome engineering using CRISPR/Cas systems. Cong, L.,    Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D.,    Wu, X., Jiang, W., Marraffini, L. A., & Zhang, F. Science Feb. 15;    339(6121):819-23 (2013);-   RNA-guided editing of bacterial genomes using CRISPR-Cas systems.    Jiang W., Bikard D., Cox D., Zhang F, Marraffini L A. Nat Biotechnol    March; 31(3):233-9 (2013);-   One-Step Generation of Mice Carrying Mutations in Multiple Genes by    CRISPR/Cas-Mediated Genome Engineering. Wang H., Yang H., Shivalila    C S., Dawlaty M M., Cheng A W., Zhang F., Jaenisch R. Cell May 9;    153(4):910-8 (2013);-   Optical control of mammalian endogenous transcription and epigenetic    states. Konermann S, Brigham M D, Trevino A E, Hsu P D, Heidenreich    M, Cong L, Platt R J, Scott D A, Church G M, Zhang F. Nature. 2013    Aug. 22; 500(7463):472-6. doi: 10.1038/Nature12466. Epub 2013 Aug.    23;-   Double Nicking by RNA-Guided CRISPR Cas9 for Enhanced Genome Editing    Specificity. Ran, F A., Hsu, P D., Lin, C Y., Gootenberg, J S.,    Konermann, S., Trevino, A E., Scott, D A., Inoue, A., Matoba, S.,    Zhang, Y., & Zhang, F. Cell August 28. pii: S0092-8674(13)01015-5.    (2013);-   DNA targeting specificity of RNA-guided Cas9 nucleases. Hsu, P.,    Scott, D., Weinstein, J., Ran, F A., Konermann, S., Agarwala, V.,    Li, Y., Fine, E., Wu, X., Shalem, O., Cradick, T J., Marraffini, L    A., Bao, G., & Zhang, F. Nat Biotechnol doi:10.1038/nbt.2647 (2013);-   Genome engineering using the CRISPR-Cas9 system. Ran, F A., Hsu, P    D., Wright, J., Agarwala, V., Scott, D A., Zhang, F. Nature    Protocols November; 8(11):2281-308. (2013);-   Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells. Shalem,    O., Sanjana, N E., Hartenian, E., Shi, X., Scott, D A., Mikkelson,    T., Heckl, D., Ebert, B L., Root, D E., Doench, J G., Zhang, F.    Science December 12. (2013). [Epub ahead of print];-   Crystal structure of cas9 in complex with guide RNA and target DNA.    Nishimasu, H., Ran, F A., Hsu, P D., Konermann, S., Shehata, S I.,    Dohmae, N., Ishitani, R., Zhang, F., Nureki, O. Cell February 27.    (2014). 156(5):935-49;-   Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian    cells. Wu X., Scott D A., Kriz A J., Chiu A C., Hsu P D., Dadon D    B., Cheng A W., Trevino A E., Konermann S., Chen S., Jaenisch R.,    Zhang F., Sharp P A. Nat Biotechnol. (2014) April 20. doi:    10.1038/nbt.2889,-   CRISPR-Cas9 Knockin Mice for Genome Editing and Cancer Modeling,    Platt et al., Cell 159(2): 440-455 (2014) DOI:    10.1016/j.cell.2014.09.014,-   Development and Applications of CRISPR-Cas9 for Genome Engineering,    Hsu et al, Cell 157, 1262-1278 (Jun. 5, 2014) (Hsu 2014),-   Genetic screens in human cells using the CRISPR/Cas9 system, Wang et    al., Science. 2014 Jan. 3; 343(6166): 80-84.    doi:10.1126/science.1246981,-   Rational design of highly active sgRNAs for CRISPR-Cas9-mediated    gene inactivation, Doench et al., Nature Biotechnology published    online 3 Sep. 2014; doi: 10.1038/nbt.3026, and-   In vivo interrogation of gene function in the mammalian brain using    CRISPR-Cas9, Swiech et al, Nature Biotechnology; published online 19    Oct. 2014; doi:10.1038/nbt.3055,    each of which is incorporated herein by reference.

The invention involves a high-throughput single-cell RNA-Seq and/ortargeted nucleic acid profiling (for example, sequencing, quantitativereverse transcription polymerase chain reaction, and the like) where theRNAs from different cells are tagged individually, allowing a singlelibrary to be created while retaining the cell identity of each read. Inthis regard, technology of U.S. provisional patent application Ser. No.62/048,227 filed Sep. 9, 2014, the disclosure of which is incorporatedby reference, may be used in or as to the invention. A combination ofmolecular barcoding and emulsion-based microfluidics to isolate, lyse,barcode, and prepare nucleic acids from individual cells inhigh-throughput is used. Microfluidic devices (for example, fabricatedin polydimethylsiloxane), sub-nanoliter reverse emulsion droplets. Thesedroplets are used to co-encapsulate nucleic acids with a barcodedcapture bead. Each bead, for example, is uniquely barcoded so that eachdrop and its contents are distinguishable. The nucleic acids may comefrom any source known in the art, such as for example, those which comefrom a single cell, a pair of cells, a cellular lysate, or a solution.The cell is lysed as it is encapsulated in the droplet. To load singlecells and barcoded beads into these droplets with Poisson statistics,100,000 to 10 million such beads are needed to barcode 10,000-100,000cells. In this regard there can be a single-cell sequencing librarywhich may comprise: merging one uniquely barcoded mRNA capture microbeadwith a single-cell in an emulsion droplet having a diameter of 75-125μm; lysing the cell to make its RNA accessible for capturing byhybridization onto RNA capture microbead; performing a reversetranscription either inside or outside the emulsion droplet to convertthe cell's mRNA to a first strand cDNA that is covalently linked to themRNA capture microbead; pooling the cDNA-attached microbeads from allcells; and preparing and sequencing a single composite RNA-Seq library.Accordingly, it is envisioned as to or in the practice of the inventionprovides that there can be a method for preparing uniquely barcoded mRNAcapture microbeads, which has a unique barcode and diameter suitable formicrofluidic devices which may comprise: 1) performing reversephosphoramidite synthesis on the surface of the bead in a pool-and-splitfashion, such that in each cycle of synthesis the beads are split intofour reactions with one of the four canonical nucleotides (T, C, G, orA) or unique oligonucleotides of length two or more bases; 2) repeatingthis process a large number of times, at least six, and optimally morethan twelve, such that, in the latter, there are more than 16 millionunique barcodes on the surface of each bead in the pool. (Seehttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC206447). Likewise, in or asto the instant invention there can be an apparatus for creating asingle-cell sequencing library via a microfluidic system, which maycomprise: an oil-surfactant inlet which may comprise a filter and acarrier fluid channel, wherein said carrier fluid channel further maycomprise a resistor; an inlet for an analyte which may comprise a filterand a carrier fluid channel, wherein said carrier fluid channel mayfurther comprise a resistor; an inlet for mRNA capture microbeads andlysis reagent which may comprise a filter and a carrier fluid channel,wherein said carrier fluid channel may further comprise a resistor; saidcarrier fluid channels have a carrier fluid flowing therein at anadjustable or predetermined flow rate; wherein each said carrier fluidchannels merge at a junction; and said junction being connected to amixer, which contains an outlet for drops. Similarly, as to or in thepractice of the instant invention there can be a method for creating asingle-cell sequencing library which may comprise: merging one uniquelybarcoded RNA capture microbead with a single-cell in an emulsion droplethaving a diameter of 125 μm lysing the cell thereby capturing the RNA onthe RNA capture microbead; performing a reverse transcription eitherafter breakage of the droplets and collection of the microbeads; orinside the emulsion droplet to convert the cell's RNA to a first strandcDNA that is covalently linked to the RNA capture microbead; pooling thecDNA-attached microbeads from all cells; and preparing and sequencing asingle composite RNA-Seq library; and, the emulsion droplet can bebetween 50-210 μm. In a further embodiment, the method wherein thediameter of the mRNA capture microbeads is from 10 μm to 95 μm. Thus,the practice of the instant invention comprehends preparing uniquelybarcoded mRNA capture microbeads, which has a unique barcode anddiameter suitable for microfluidic devices which may comprise: 1)performing reverse phosphoramidite synthesis on the surface of the beadin a pool-and-split fashion, such that in each cycle of synthesis thebeads are split into four reactions with one of the four canonicalnucleotides (T, C, G, or A); 2) repeating this process a large number oftimes, at least six, and optimally more than twelve, such that, in thelatter, there are more than 16 million unique barcodes on the surface ofeach bead in the pool. The covalent bond can be polyethylene glycol. Thediameter of the mRNA capture microbeads can be from 10 μm to 95 μm.Accordingly, it is also envisioned as to or in the practice of theinvention that there can be a method for preparing uniquely barcodedmRNA capture microbeads, which has a unique barcode and diametersuitable for microfluidic devices which may comprise: 1) performingreverse phosphoramidite synthesis on the surface of the bead in apool-and-split fashion, such that in each cycle of synthesis the beadsare split into four reactions with one of the four canonical nucleotides(T, C, G, or A); 2) repeating this process a large number of times, atleast six, and optimally more than twelve, such that, in the latter,there are more than 16 million unique barcodes on the surface of eachbead in the pool. And, the diameter of the mRNA capture microbeads canbe from 10 μm to 95 μm. Further, as to in the practice of the inventionthere can be an apparatus for creating a composite single-cellsequencing library via a microfluidic system, which may comprise: anoil-surfactant inlet which may comprise a filter and two carrier fluidchannels, wherein said carrier fluid channel further may comprise aresistor; an inlet for an analyte which may comprise a filter and twocarrier fluid channels, wherein said carrier fluid channel further maycomprise a resistor; an inlet for mRNA capture microbeads and lysisreagent which may comprise a carrier fluid channel; said carrier fluidchannels have a carrier fluid flowing therein at an adjustable andpredetermined flow rate; wherein each said carrier fluid channels mergeat a junction; and said junction being connected to a constriction fordroplet pinch-off followed by a mixer, which connects to an outlet fordrops. The analyte may comprise a chemical reagent, a geneticallyperturbed cell, a protein, a drug, an antibody, an enzyme, a nucleicacid, an organelle like the mitochondrion or nucleus, a cell or anycombination thereof. In an embodiment of the apparatus the analyte is acell. In a further embodiment the cell is a brain cell. In an embodimentof the apparatus the lysis reagent may comprise an anionic surfactantsuch as sodium lauroyl sarcosinate, or a chaotropic salt such asguanidinium thiocyanate. The filter can involve square PDMS posts; e.g.,with the filter on the cell channel of such posts with sides rangingbetween 125-135 μm with a separation of 70-100 mm between the posts. Thefilter on the oil-surfactant inlet may comprise square posts of twosizes; one with sides ranging between 75-100 μm and a separation of25-30 μm between them and the other with sides ranging between 40-50 μmand a separation of 10-15 μm. The apparatus can involve a resistor,e.g., a resistor that is serpentine having a length of 7000-9000 μm,width of 50-75 μm and depth of 100-150 mm. The apparatus can havechannels having a length of 8000-12,000 μm for oil-surfactant inlet,5000-7000 for analyte (cell) inlet, and 900-1200 μm for the inlet formicrobead and lysis agent; and/or all channels having a width of 125-250mm, and depth of 100-150 mm. The width of the cell channel can be125-250 μm and the depth 100-150 μm. The apparatus can include a mixerhaving a length of 7000-9000 μm, and a width of 110-140 μm with 35-45ozig-zigs every 150 μm. The width of the mixer can be about 125 μm. Theoil-surfactant can be a PEG Block Polymer, such as BIORAD™ QX200 DropletGeneration Oil. The carrier fluid can be a water-glycerol mixture. Inthe practice of the invention or as to the invention, a mixture maycomprise a plurality of microbeads adorned with combinations of thefollowing elements: bead-specific oligonucleotide barcodes; additionaloligonucleotide barcode sequences which vary among the oligonucleotideson an individual bead and can therefore be used to differentiate or helpidentify those individual oligonucleotide molecules; additionaloligonucleotide sequences that create substrates for downstreammolecular-biological reactions, such as oligo-dT (for reversetranscription of mature mRNAs), specific sequences (for capturingspecific portions of the transcriptome, or priming for DNA polymerasesand similar enzymes), or random sequences (for priming throughout thetranscriptome or genome). The individual oligonucleotide molecules onthe surface of any individual microbead may contain all three of theseelements, and the third element may include both oligo-dT and a primersequence. A mixture may comprise a plurality of microbeads, wherein saidmicrobeads may comprise the following elements: at least onebead-specific oligonucleotide barcode; at least one additionalidentifier oligonucleotide barcode sequence, which varies among theoligonucleotides on an individual bead, and thereby assisting in theidentification and of the bead specific oligonucleotide molecules;optionally at least one additional oligonucleotide sequences, whichprovide substrates for downstream molecular-biological reactions. Amixture may comprise at least one oligonucleotide sequence(s), whichprovide for substrates for downstream molecular-biological reactions. Ina further embodiment the downstream molecular biological reactions arefor reverse transcription of mature mRNAs; capturing specific portionsof the transcriptome, priming for DNA polymerases and/or similarenzymes; or priming throughout the transcriptome or genome. The mixturemay involve additional oligonucleotide sequence(s) which may comprise aoligio-dT sequence. The mixture further may comprise the additionaloligonucleotide sequence which may comprise a primer sequence. Themixture may further comprise the additional oligonucleotide sequencewhich may comprise a oligo-dT sequence and a primer sequence. Examplesof the labeling substance which may be employed include labelingsubstances known to those skilled in the art, such as fluorescent dyes,enzymes, coenzymes, chemiluminescent substances, and radioactivesubstances. Specific examples include radioisotopes (e.g., 32P, 14C,125I, 3H, and 131I), fluorescein, rhodamine, dansyl chloride,umbelliferone, luciferase, peroxidase, alkaline phosphatase,β-galactosidase, β-glucosidase, horseradish peroxidase, glucoamylase,lysozyme, saccharide oxidase, microperoxidase, biotin, and ruthenium. Inthe case where biotin is employed as a labeling substance, preferably,after addition of a biotin-labeled antibody, streptavidin bound to anenzyme (e.g., peroxidase) is further added. Advantageously, the label isa fluorescent label. Examples of fluorescent labels include, but are notlimited to, Atto dyes,4-acetamido-4′-isothiocyanatostilbene-2,2′disulfonic acid; acridine andderivatives: acridine, acridine isothiocyanate;5-(2′-aminoethyl)aminonaphthalene-1-sulfonic acid (EDANS);4-amino-N-[3-vinyl sulfonyl)phenyl]naphthalimide-3,5 disulfonate;N-(4-anilino-1-naphthyl)maleimide; anthranilamide; BODIPY; BrilliantYellow; coumarin and derivatives; coumarin, 7-amino-4-methylcoumarin(AMC, Coumarin 120), 7-amino-4-trifluoromethylcouluarin (Coumaran 151);cyanine dyes; cyanosine; 4′,6-diaminidino-2-phenylindole (DAPI);5′5″-dibromopyrogallol-sulfonaphthalein (Bromopyrogallol Red);7-diethylamino-3-(4′-isothiocyanatophenyl)-4-methylcoumarin;diethylenetriamine pentaacetate;4,4′-diisothiocyanatodihydro-stilbene-2,2′-disulfonic acid;4,4′-diisothiocyanatostilbene-2,2′-disulfonic acid;5-[dimethylamino]naphthalene-1-sulfonyl chloride (DNS, dansylchloride);4-dimethylaminophenylazophenyl-4′-isothiocyanate (DABITC); eosin andderivatives; eosin, eosin isothiocyanate, erythrosin and derivatives;erythrosin B, erythrosin, isothiocyanate; ethidium; fluorescein andderivatives; 5-carboxyfluorescein (FAM),5-(4,6-dichlorotriazin-2-yl)aminofluorescein (DTAF),2′,7′-dimethoxy-4′5′-dichloro-6-carboxyfluorescein, fluorescein,fluorescein isothiocyanate, QFITC, (XRITC); fluorescamine; IR144;IR1446; Malachite Green isothiocyanate; 4-methylumbelliferoneorthocresolphthalein; nitrotyrosine; pararosaniline; Phenol Red;B-phycoerythrin; o-phthaldialdehyde; pyrene and derivatives: pyrene,pyrene butyrate, succinimidyl 1-pyrene; butyrate quantum dots; ReactiveRed 4 (Cibacron™ Brilliant Red 3B-A) rhodamine and derivatives:6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), lissaminerhodamine B sulfonyl chloride rhodamine (Rhod), rhodamine B, rhodamine123, rhodamine X isothiocyanate, sulforhodamine B, sulforhodamine 101,sulfonyl chloride derivative of sulforhodamine 101 (Texas Red);N,N,N′,N′ tetramethyl-6-carboxyrhodamine (TAMRA); tetramethyl rhodamine;tetramethyl rhodamine isothiocyanate (TRITC); riboflavin; rosolic acid;terbium chelate derivatives; Cy3; Cy5; Cy5.5; Cy7; IRD 700; IRD 800; LaJolta Blue; phthalo cyanine; and naphthalo cyanine. A fluorescent labelmay be a fluorescent protein, such as blue fluorescent protein, cyanfluorescent protein, green fluorescent protein, red fluorescent protein,yellow fluorescent protein or any photoconvertible protein. Colormetriclabeling, bioluminescent labeling and/or chemiluminescent labeling mayfurther accomplish labeling. Labeling further may include energytransfer between molecules in the hybridization complex by perturbationanalysis, quenching, or electron transport between donor and acceptormolecules, the latter of which may be facilitated by double strandedmatch hybridization complexes. The fluorescent label may be a peryleneor a terrylen. In the alternative, the fluorescent label may be afluorescent bar code. Advantageously, the label may be light sensitive,wherein the label is light-activated and/or light cleaves the one ormore linkers to release the molecular cargo. The light-activatedmolecular cargo may be a major light-harvesting complex (LHCII). Inanother embodiment, the fluorescent label may induce free radicalformation. Advantageously, agents may be uniquely labeled in a dynamicmanner (see, e.g., U.S. provisional patent application Ser. No.61/703,884 filed Sep. 21, 2012). The unique labels are, at least inpart, nucleic acid in nature, and may be generated by sequentiallyattaching two or more detectable oligonucleotide tags to each other andeach unique label may be associated with a separate agent. A detectableoligonucleotide tag may be an oligonucleotide that may be detected bysequencing of its nucleotide sequence and/or by detecting non-nucleicacid detectable moieties to which it may be attached. Oligonucleotidetags may be detectable by virtue of their nucleotide sequence, or byvirtue of a non-nucleic acid detectable moiety that is attached to theoligonucleotide such as but not limited to a fluorophore, or by virtueof a combination of their nucleotide sequence and the nonnucleic aciddetectable moiety. A detectable oligonucleotide tag may comprise one ormore nonoligonucleotide detectable moieties. Examples of detectablemoieties may include, but are not limited to, fluorophores,microparticles including quantum dots (Empodocles, et al., Nature399:126-130, 1999), gold nanoparticles (Reichert et al., Anal. Chem.72:6025-6029, 2000), microbeads (Lacoste et al., Proc. Natl. Acad. Sci.USA 97(17):9461-9466, 2000), biotin, DNP (dinitrophenyl), fucose,digoxigenin, haptens, and other detectable moieties known to thoseskilled in the art. In some embodiments, the detectable moieties may bequantum dots. Methods for detecting such moieties are described hereinand/or are known in the art. Thus, detectable oligonucleotide tags maybe, but are not limited to, oligonucleotides which may comprise uniquenucleotide sequences, oligonucleotides which may comprise detectablemoieties, and oligonucleotides which may comprise both unique nucleotidesequences and detectable moieties. A unique label may be produced bysequentially attaching two or more detectable oligonucleotide tags toeach other. The detectable tags may be present or provided in aplurality of detectable tags. The same or a different plurality of tagsmay be used as the source of each detectable tag may be part of a uniquelabel. In other words, a plurality of tags may be subdivided intosubsets and single subsets may be used as the source for each tag. Oneor more other species may be associated with the tags. In particular,nucleic acids released by a lysed cell may be ligated to one or moretags. These may include, for example, chromosomal DNA, RNA transcripts,tRNA, mRNA, mitochondrial DNA, or the like. Such nucleic acids may besequenced, in addition to sequencing the tags themselves, which mayyield information about the nucleic acid profile of the cells, which canbe associated with the tags, or the conditions that the correspondingdroplet or cell was exposed to.

The invention accordingly may involve or be practiced as to highthroughput and high resolution delivery of reagents to individualemulsion droplets that may contain cells, organelles, nucleic acids,proteins, etc. through the use of monodisperse aqueous droplets that aregenerated by a microfluidic device as a water-in-oil emulsion. Thedroplets are carried in a flowing oil phase and stabilized by asurfactant. In one aspect single cells or single organelles or singlemolecules (proteins, RNA, DNA) are encapsulated into uniform dropletsfrom an aqueous solution/dispersion. In a related aspect, multiple cellsor multiple molecules may take the place of single cells or singlemolecules. The aqueous droplets of volume ranging from 1 pL to 10 nLwork as individual reactors. 104 to 105 single cells in droplets may beprocessed and analyzed in a single run. To utilize microdroplets forrapid large-scale chemical screening or complex biological libraryidentification, different species of microdroplets, each containing thespecific chemical compounds or biological probes cells or molecularbarcodes of interest, have to be generated and combined at the preferredconditions, e.g., mixing ratio, concentration, and order of combination.Each species of droplet is introduced at a confluence point in a mainmicrofluidic channel from separate inlet microfluidic channels.Preferably, droplet volumes are chosen by design such that one speciesis larger than others and moves at a different speed, usually slowerthan the other species, in the carrier fluid, as disclosed in U.S.Publication No. US 2007/0195127 and International Publication No. WO2007/089541, each of which are incorporated herein by reference in theirentirety. The channel width and length is selected such that fasterspecies of droplets catch up to the slowest species. Size constraints ofthe channel prevent the faster moving droplets from passing the slowermoving droplets resulting in a train of droplets entering a merge zone.Multi-step chemical reactions, biochemical reactions, or assay detectionchemistries often require a fixed reaction time before species ofdifferent type are added to a reaction. Multi-step reactions areachieved by repeating the process multiple times with a second, third ormore confluence points each with a separate merge point. Highlyefficient and precise reactions and analysis of reactions are achievedwhen the frequencies of droplets from the inlet channels are matched toan optimized ratio and the volumes of the species are matched to provideoptimized reaction conditions in the combined droplets. Fluidic dropletsmay be screened or sorted within a fluidic system of the invention byaltering the flow of the liquid containing the droplets. For instance,in one set of embodiments, a fluidic droplet may be steered or sorted bydirecting the liquid surrounding the fluidic droplet into a firstchannel, a second channel, etc. In another set of embodiments, pressurewithin a fluidic system, for example, within different channels orwithin different portions of a channel, can be controlled to direct theflow of fluidic droplets. For example, a droplet can be directed towarda channel junction including multiple options for further direction offlow (e.g., directed toward a branch, or fork, in a channel definingoptional downstream flow channels). Pressure within one or more of theoptional downstream flow channels can be controlled to direct thedroplet selectively into one of the channels, and changes in pressurecan be effected on the order of the time required for successivedroplets to reach the junction, such that the downstream flow path ofeach successive droplet can be independently controlled. In onearrangement, the expansion and/or contraction of liquid reservoirs maybe used to steer or sort a fluidic droplet into a channel, e.g., bycausing directed movement of the liquid containing the fluidic droplet.In another, the expansion and/or contraction of the liquid reservoir maybe combined with other flow-controlling devices and methods, e.g., asdescribed herein. Non-limiting examples of devices able to cause theexpansion and/or contraction of a liquid reservoir include pistons. Keyelements for using microfluidic channels to process droplets include:(1) producing droplet of the correct volume, (2) producing droplets atthe correct frequency and (3) bringing together a first stream of sampledroplets with a second stream of sample droplets in such a way that thefrequency of the first stream of sample droplets matches the frequencyof the second stream of sample droplets. Preferably, bringing together astream of sample droplets with a stream of premade library droplets insuch a way that the frequency of the library droplets matches thefrequency of the sample droplets. Methods for producing droplets of auniform volume at a regular frequency are well known in the art. Onemethod is to generate droplets using hydrodynamic focusing of adispersed phase fluid and immiscible carrier fluid, such as disclosed inU.S. Publication No. US 2005/0172476 and International Publication No.WO 2004/002627. It is desirable for one of the species introduced at theconfluence to be a pre-made library of droplets where the librarycontains a plurality of reaction conditions, e.g., a library may containplurality of different compounds at a range of concentrationsencapsulated as separate library elements for screening their effect oncells or enzymes, alternatively a library could be composed of aplurality of different primer pairs encapsulated as different libraryelements for targeted amplification of a collection of loci,alternatively a library could contain a plurality of different antibodyspecies encapsulated as different library elements to perform aplurality of binding assays. The introduction of a library of reactionconditions onto a substrate is achieved by pushing a premade collectionof library droplets out of a vial with a drive fluid. The drive fluid isa continuous fluid. The drive fluid may comprise the same substance asthe carrier fluid (e.g., a fluorocarbon oil). For example, if a libraryconsists of ten pico-liter droplets is driven into an inlet channel on amicrofluidic substrate with a drive fluid at a rate of 10,000pico-liters per second, then nominally the frequency at which thedroplets are expected to enter the confluence point is 1000 per second.However, in practice droplets pack with oil between them that slowlydrains. Over time the carrier fluid drains from the library droplets andthe number density of the droplets (number/mL) increases. Hence, asimple fixed rate of infusion for the drive fluid does not provide auniform rate of introduction of the droplets into the microfluidicchannel in the substrate. Moreover, library-to-library variations in themean library droplet volume result in a shift in the frequency ofdroplet introduction at the confluence point. Thus, the lack ofuniformity of droplets that results from sample variation and oildrainage provides another problem to be solved. For example if thenominal droplet volume is expected to be 10 pico-liters in the library,but varies from 9 to 11 pico-liters from library-to-library then a10,000 pico-liter/second infusion rate will nominally produce a range infrequencies from 900 to 1,100 droplet per second. In short, sample tosample variation in the composition of dispersed phase for droplets madeon chip, a tendency for the number density of library droplets toincrease over time and library-to-library variations in mean dropletvolume severely limit the extent to which frequencies of droplets may bereliably matched at a confluence by simply using fixed infusion rates.In addition, these limitations also have an impact on the extent towhich volumes may be reproducibly combined. Combined with typicalvariations in pump flow rate precision and variations in channeldimensions, systems are severely limited without a means to compensateon a run-to-run basis. The foregoing facts not only illustrate a problemto be solved, but also demonstrate a need for a method of instantaneousregulation of microfluidic control over microdroplets within amicrofluidic channel. Combinations of surfactant(s) and oils must bedeveloped to facilitate generation, storage, and manipulation ofdroplets to maintain the unique chemical/biochemical/biologicalenvironment within each droplet of a diverse library. Therefore, thesurfactant and oil combination must (1) stabilize droplets againstuncontrolled coalescence during the drop forming process and subsequentcollection and storage, (2) minimize transport of any droplet contentsto the oil phase and/or between droplets, and (3) maintain chemical andbiological inertness with contents of each droplet (e.g., no adsorptionor reaction of encapsulated contents at the oil-water interface, and noadverse effects on biological or chemical constituents in the droplets).In addition to the requirements on the droplet library function andstability, the surfactant-in-oil solution must be coupled with the fluidphysics and materials associated with the platform. Specifically, theoil solution must not swell, dissolve, or degrade the materials used toconstruct the microfluidic chip, and the physical properties of the oil(e.g., viscosity, boiling point, etc.) must be suited for the flow andoperating conditions of the platform. Droplets formed in oil withoutsurfactant are not stable to permit coalescence, so surfactants must bedissolved in the oil that is used as the continuous phase for theemulsion library. Surfactant molecules are amphiphilic—part of themolecule is oil soluble, and part of the molecule is water soluble. Whena water-oil interface is formed at the nozzle of a microfluidic chip forexample in the inlet module described herein, surfactant molecules thatare dissolved in the oil phase adsorb to the interface. The hydrophilicportion of the molecule resides inside the droplet and the fluorophilicportion of the molecule decorates the exterior of the droplet. Thesurface tension of a droplet is reduced when the interface is populatedwith surfactant, so the stability of an emulsion is improved. Inaddition to stabilizing the droplets against coalescence, the surfactantshould be inert to the contents of each droplet and the surfactantshould not promote transport of encapsulated components to the oil orother droplets. A droplet library may be made up of a number of libraryelements that are pooled together in a single collection (see, e.g., USPatent Publication No. 2010002241). Libraries may vary in complexityfrom a single library element to 1015 library elements or more. Eachlibrary element may be one or more given components at a fixedconcentration. The element may be, but is not limited to, cells,organelles, virus, bacteria, yeast, beads, amino acids, proteins,polypeptides, nucleic acids, polynucleotides or small molecule chemicalcompounds. The element may contain an identifier such as a label. Theterms “droplet library” or “droplet libraries” are also referred toherein as an “emulsion library” or “emulsion libraries.” These terms areused interchangeably throughout the specification. A cell libraryelement may include, but is not limited to, hybridomas, B-cells, primarycells, cultured cell lines, cancer cells, stem cells, cells obtainedfrom tissue, or any other cell type. Cellular library elements areprepared by encapsulating a number of cells from one to hundreds ofthousands in individual droplets. The number of cells encapsulated isusually given by Poisson statistics from the number density of cells andvolume of the droplet. However, in some cases the number deviates fromPoisson statistics as described in Edd et al., “Controlled encapsulationof single-cells into monodisperse picolitre drops.” Lab Chip, 8(8):1262-1264, 2008. The discrete nature of cells allows for libraries to beprepared in mass with a plurality of cellular variants all present in asingle starting media and then that media is broken up into individualdroplet capsules that contain at most one cell. These individualdroplets capsules are then combined or pooled to form a libraryconsisting of unique library elements. Cell division subsequent to, orin some embodiments following, encapsulation produces a clonal libraryelement. A bead based library element may contain one or more beads, ofa given type and may also contain other reagents, such as antibodies,enzymes or other proteins. In the case where all library elementscontain different types of beads, but the same surrounding media, thelibrary elements may all be prepared from a single starting fluid orhave a variety of starting fluids. In the case of cellular librariesprepared in mass from a collection of variants, such as genomicallymodified, yeast or bacteria cells, the library elements will be preparedfrom a variety of starting fluids. Often it is desirable to have exactlyone cell per droplet with only a few droplets containing more than onecell when starting with a plurality of cells or yeast or bacteria,engineered to produce variants on a protein. In some cases, variationsfrom Poisson statistics may be achieved to provide an enhanced loadingof droplets such that there are more droplets with exactly one cell perdroplet and few exceptions of empty droplets or droplets containing morethan one cell. Examples of droplet libraries are collections of dropletsthat have different contents, ranging from beads, cells, smallmolecules, DNA, primers, antibodies. Smaller droplets may be in theorder of femtoliter (fL) volume drops, which are especially contemplatedwith the droplet dispensors. The volume may range from about 5 to about600 fL. The larger droplets range in size from roughly 0.5 micron to 500micron in diameter, which corresponds to about 1 pico liter to 1 nanoliter. However, droplets may be as small as 5 microns and as large as500 microns. Preferably, the droplets are at less than 100 microns,about 1 micron to about 100 microns in diameter. The most preferred sizeis about 20 to 40 microns in diameter (10 to 100 picoliters). Thepreferred properties examined of droplet libraries include osmoticpressure balance, uniform size, and size ranges. The droplets within theemulsion libraries of the present invention may be contained within animmiscible oil which may comprise at least one fluorosurfactant. In someembodiments, the fluorosurfactant within the immiscible fluorocarbon oilmay be a block copolymer consisting of one or more perfluorinatedpolyether (PFPE) blocks and one or more polyethylene glycol (PEG)blocks. In other embodiments, the fluorosurfactant is a triblockcopolymer consisting of a PEG center block covalently bound to two PFPEblocks by amide linking groups. The presence of the fluorosurfactant(similar to uniform size of the droplets in the library) is critical tomaintain the stability and integrity of the droplets and is alsoessential for the subsequent use of the droplets within the library forthe various biological and chemical assays described herein. Fluids(e.g., aqueous fluids, immiscible oils, etc.) and other surfactants thatmay be utilized in the droplet libraries of the present invention aredescribed in greater detail herein. The present invention canaccordingly involve an emulsion library which may comprise a pluralityof aqueous droplets within an immiscible oil (e.g., fluorocarbon oil)which may comprise at least one fluorosurfactant, wherein each dropletis uniform in size and may comprise the same aqueous fluid and maycomprise a different library element. The present invention alsoprovides a method for forming the emulsion library which may compriseproviding a single aqueous fluid which may comprise different libraryelements, encapsulating each library element into an aqueous dropletwithin an immiscible fluorocarbon oil which may comprise at least onefluorosurfactant, wherein each droplet is uniform in size and maycomprise the same aqueous fluid and may comprise a different libraryelement, and pooling the aqueous droplets within an immisciblefluorocarbon oil which may comprise at least one fluorosurfactant,thereby forming an emulsion library. For example, in one type ofemulsion library, all different types of elements (e.g., cells orbeads), may be pooled in a single source contained in the same medium.After the initial pooling, the cells or beads are then encapsulated indroplets to generate a library of droplets wherein each droplet with adifferent type of bead or cell is a different library element. Thedilution of the initial solution enables the encapsulation process. Insome embodiments, the droplets formed will either contain a single cellor bead or will not contain anything, i.e., be empty. In otherembodiments, the droplets formed will contain multiple copies of alibrary element. The cells or beads being encapsulated are generallyvariants on the same type of cell or bead. In another example, theemulsion library may comprise a plurality of aqueous droplets within animmiscible fluorocarbon oil, wherein a single molecule may beencapsulated, such that there is a single molecule contained within adroplet for every 20-60 droplets produced (e.g., 20, 25, 30, 35, 40, 45,50, 55, 60 droplets, or any integer in between). Single molecules may beencapsulated by diluting the solution containing the molecules to such alow concentration that the encapsulation of single molecules is enabled.In one specific example, a LacZ plasmid DNA was encapsulated at aconcentration of 20 fM after two hours of incubation such that there wasabout one gene in 40 droplets, where 10 μm droplets were made at 10 kHzper second. Formation of these libraries rely on limiting dilutions.

The present invention also provides an emulsion library which maycomprise at least a first aqueous droplet and at least a second aqueousdroplet within a fluorocarbon oil which may comprise at least onefluorosurfactant, wherein the at least first and the at least seconddroplets are uniform in size and comprise a different aqueous fluid anda different library element. The present invention also provides amethod for forming the emulsion library which may comprise providing atleast a first aqueous fluid which may comprise at least a first libraryof elements, providing at least a second aqueous fluid which maycomprise at least a second library of elements, encapsulating eachelement of said at least first library into at least a first aqueousdroplet within an immiscible fluorocarbon oil which may comprise atleast one fluorosurfactant, encapsulating each element of said at leastsecond library into at least a second aqueous droplet within animmiscible fluorocarbon oil which may comprise at least onefluorosurfactant, wherein the at least first and the at least seconddroplets are uniform in size and may comprise a different aqueous fluidand a different library element, and pooling the at least first aqueousdroplet and the at least second aqueous droplet within an immisciblefluorocarbon oil which may comprise at least one fluorosurfactantthereby forming an emulsion library. One of skill in the art willrecognize that methods and systems of the invention are not preferablypracticed as to cells, mutations, etc as herein disclosed, but that theinvention need not be limited to any particular type of sample, andmethods and systems of the invention may be used with any type oforganic, inorganic, or biological molecule (see, e.g, US PatentPublication No. 20120122714). In particular embodiments the sample mayinclude nucleic acid target molecules. Nucleic acid molecules may besynthetic or derived from naturally occurring sources. In oneembodiment, nucleic acid molecules may be isolated from a biologicalsample containing a variety of other components, such as proteins,lipids and non-template nucleic acids. Nucleic acid target molecules maybe obtained from any cellular material, obtained from an animal, plant,bacterium, fungus, or any other cellular organism. In certainembodiments, the nucleic acid target molecules may be obtained from asingle cell. Biological samples for use in the present invention mayinclude viral particles or preparations. Nucleic acid target moleculesmay be obtained directly from an organism or from a biological sampleobtained from an organism, e.g., from blood, urine, cerebrospinal fluid,seminal fluid, saliva, sputum, stool and tissue. Any tissue or bodyfluid specimen may be used as a source for nucleic acid for use in theinvention. Nucleic acid target molecules may also be isolated fromcultured cells, such as a primary cell culture or a cell line. The cellsor tissues from which target nucleic acids are obtained may be infectedwith a virus or other intracellular pathogen. A sample may also be totalRNA extracted from a biological specimen, a cDNA library, viral, orgenomic DNA. Generally, nucleic acid may be extracted from a biologicalsample by a variety of techniques such as those described by Maniatis,et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbor,N.Y., pp. 280-281 (1982). Nucleic acid molecules may be single-stranded,double-stranded, or double-stranded with single-stranded regions (forexample, stem- and loop-structures). Nucleic acid obtained frombiological samples typically may be fragmented to produce suitablefragments for analysis. Target nucleic acids may be fragmented orsheared to desired length, using a variety of mechanical, chemicaland/or enzymatic methods. DNA may be randomly sheared via sonication,e.g. Covaris method, brief exposure to a DNase, or using a mixture ofone or more restriction enzymes, or a transposase or nicking enzyme. RNAmay be fragmented by brief exposure to an RNase, heat plus magnesium, orby shearing. The RNA may be converted to cDNA. If fragmentation isemployed, the RNA may be converted to cDNA before or afterfragmentation. In one embodiment, nucleic acid from a biological sampleis fragmented by sonication. In another embodiment, nucleic acid isfragmented by a hydroshear instrument. Generally, individual nucleicacid target molecules may be from about 40 bases to about 40 kb. Nucleicacid molecules may be single-stranded, double-stranded, ordouble-stranded with single-stranded regions (for example, stem- andloop-structures). A biological sample as described herein may behomogenized or fractionated in the presence of a detergent orsurfactant. The concentration of the detergent in the buffer may beabout 0.05% to about 10.0%. The concentration of the detergent may be upto an amount where the detergent remains soluble in the solution. In oneembodiment, the concentration of the detergent is between 0.1% to about2%. The detergent, particularly a mild one that is nondenaturing, mayact to solubilize the sample. Detergents may be ionic or nonionic.Examples of nonionic detergents include triton, such as the Triton™ Xseries (Triton™ X-100 t-Oct-C6H4-(OCH2-CH2)xOH, x=9-10, Triton™ X-100R,Triton™ X-114 x=7-8), octyl glucoside, polyoxyethylene(9)dodecyl ether,digitonin, IGEPAL™ CA630 octylphenyl polyethylene glycol,n-octyl-beta-D-glucopyranoside (betaOG), n-dodecyl-beta, Tween™. 20polyethylene glycol sorbitan monolaurate, Tween™ 80 polyethylene glycolsorbitan monooleate, polidocanol, n-dodecyl beta-D-maltoside (DDM),NP-40 nonylphenyl polyethylene glycol, C12E8 (octaethylene glycoln-dodecyl monoether), hexaethyleneglycol mono-n-tetradecyl ether(C14E06), octyl-beta-thioglucopyranoside (octyl thioglucoside, OTG),Emulgen, and polyoxyethylene 10 lauryl ether (C12E10). Examples of ionicdetergents (anionic or cationic) include deoxycholate, sodium dodecylsulfate (SDS), N-lauroylsarcosine, and cetyltrimethylammoniumbromide(CTAB). A zwitterionic reagent may also be used in the purificationschemes of the present invention, such as Chaps, zwitterion 3-14, and3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulf-onate. It iscontemplated also that urea may be added with or without anotherdetergent or surfactant. Lysis or homogenization solutions may furthercontain other agents, such as reducing agents. Examples of such reducingagents include dithiothreitol (DTT), β-mercaptoethanol, DTE, GSH,cysteine, cysteamine, tricarboxyethyl phosphine (TCEP), or salts ofsulfurous acid. Size selection of the nucleic acids may be performed toremove very short fragments or very long fragments. The nucleic acidfragments may be partitioned into fractions which may comprise a desirednumber of fragments using any suitable method known in the art. Suitablemethods to limit the fragment size in each fragment are known in theart. In various embodiments of the invention, the fragment size islimited to between about 10 and about 100 Kb or longer. A sample in oras to the instant invention may include individual target proteins,protein complexes, proteins with translational modifications, andprotein/nucleic acid complexes. Protein targets include peptides, andalso include enzymes, hormones, structural components such as viralcapsid proteins, and antibodies. Protein targets may be synthetic orderived from naturally-occurring sources. The invention protein targetsmay be isolated from biological samples containing a variety of othercomponents including lipids, non-template nucleic acids, and nucleicacids. Protein targets may be obtained from an animal, bacterium,fungus, cellular organism, and single cells. Protein targets may beobtained directly from an organism or from a biological sample obtainedfrom the organism, including bodily fluids such as blood, urine,cerebrospinal fluid, seminal fluid, saliva, sputum, stool and tissue.Protein targets may also be obtained from cell and tissue lysates andbiochemical fractions. An individual protein is an isolated polypeptidechain. A protein complex includes two or polypeptide chains. Samples mayinclude proteins with post translational modifications including but notlimited to phosphorylation, methionine oxidation, deamidation,glycosylation, ubiquitination, carbamylation, s-carboxymethylation,acetylation, and methylation. Protein/nucleic acid complexes includecross-linked or stable protein-nucleic acid complexes. Extraction orisolation of individual proteins, protein complexes, proteins withtranslational modifications, and protein/nucleic acid complexes isperformed using methods known in the art.

The invention can thus involve forming sample droplets. The droplets areaqueous droplets that are surrounded by an immiscible carrier fluid.Methods of forming such droplets are shown for example in Link et al.(U.S. patent application numbers 2008/0014589, 2008/0003142, and2010/0137163), Stone et al. (U.S. Pat. No. 7,708,949 and U.S. patentapplication number 2010/0172803), Anderson et al. (U.S. Pat. No.7,041,481 and which reissued as RE41,780) and European publicationnumber EP2047910 to Raindance Technologies Inc. The content of each ofwhich is incorporated by reference herein in its entirety. The presentinvention may relates to systems and methods for manipulating dropletswithin a high throughput microfluidic system. A microfluid dropletencapsulates a differentiated cell The cell is lysed and its mRNA ishybridized onto a capture bead containing barcoded oligo dT primers onthe surface, all inside the droplet. The barcode is covalently attachedto the capture bead via a flexible multi-atom linker like PEG. In apreferred embodiment, the droplets are broken by addition of afluorosurfactant (like perfluorooctanol), washed, and collected. Areverse transcription (RT) reaction is then performed to convert eachcell's mRNA into a first strand cDNA that is both uniquely barcoded andcovalently linked to the mRNA capture bead. Subsequently, a universalprimer via a template switching reaction is amended using conventionallibrary preparation protocols to prepare an RNA-Seq library. Since allof the mRNA from any given cell is uniquely barcoded, a single libraryis sequenced and then computationally resolved to determine which mRNAscame from which cells. In this way, through a single sequencing run,tens of thousands (or more) of distinguishable transcriptomes can besimultaneously obtained. The oligonucleotide sequence may be generatedon the bead surface. During these cycles, beads were removed from thesynthesis column, pooled, and aliquoted into four equal portions bymass; these bead aliquots were then placed in a separate synthesiscolumn and reacted with either dG, dC, dT, or dA phosphoramidite. Inother instances, dinucleotide, trinucleotides, or oligonucleotides thatare greater in length are used, in other instances, the oligo-dT tail isreplaced by gene specific oligonucleotides to prime specific targets(singular or plural), random sequences of any length for the capture ofall or specific RNAs. This process was repeated 12 times for a total of4¹²=16,777,216 unique barcode sequences. Upon completion of thesecycles, 8 cycles of degenerate oligonucleotide synthesis were performedon all the beads, followed by 30 cycles of dT addition. In otherembodiments, the degenerate synthesis is omitted, shortened (less than 8cycles), or extended (more than 8 cycles); in others, the 30 cycles ofdT addition are replaced with gene specific primers (single target ormany targets) or a degenerate sequence. The aforementioned microfluidicsystem is regarded as the reagent delivery system microfluidic libraryprinter or droplet library printing system of the present invention.Droplets are formed as sample fluid flows from droplet generator whichcontains lysis reagent and barcodes through microfluidic outlet channelwhich contains oil, towards junction. Defined volumes of loaded reagentemulsion, corresponding to defined numbers of droplets, are dispensedon-demand into the flow stream of carrier fluid. The sample fluid maytypically comprise an aqueous buffer solution, such as ultrapure water(e.g., 18 mega-ohm resistivity, obtained, for example by columnchromatography), 10 mM Tris HCl and 1 mM EDTA (TE) buffer, phosphatebuffer saline (PBS) or acetate buffer. Any liquid or buffer that isphysiologically compatible with nucleic acid molecules can be used. Thecarrier fluid may include one that is immiscible with the sample fluid.The carrier fluid can be a non-polar solvent, decane (e.g., tetradecaneor hexadecane), fluorocarbon oil, silicone oil, an inert oil such ashydrocarbon, or another oil (for example, mineral oil). The carrierfluid may contain one or more additives, such as agents which reducesurface tensions (surfactants). Surfactants can include Tween, Span,fluorosurfactants, and other agents that are soluble in oil relative towater. In some applications, performance is improved by adding a secondsurfactant to the sample fluid. Surfactants can aid in controlling oroptimizing droplet size, flow and uniformity, for example by reducingthe shear force needed to extrude or inject droplets into anintersecting channel. This can affect droplet volume and periodicity, orthe rate or frequency at which droplets break off into an intersectingchannel. Furthermore, the surfactant can serve to stabilize aqueousemulsions in fluorinated oils from coalescing. Droplets may besurrounded by a surfactant which stabilizes the droplets by reducing thesurface tension at the aqueous oil interface. Preferred surfactants thatmay be added to the carrier fluid include, but are not limited to,surfactants such as sorbitan-based carboxylic acid esters (e.g., the“Span” surfactants, Fluka Chemika), including sorbitan monolaurate (Span20), sorbitan monopalmitate (Span 40), sorbitan monostearate (Span 60)and sorbitan monooleate (Span 80), and perfluorinated polyethers (e.g.,DuPont Krytox 157 FSL, FSM, and/or FSH). Other non-limiting examples ofnon-ionic surfactants which may be used include polyoxyethylenatedalkylphenols (for example, nonyl-, p-dodecyl-, and dinonylphenols),polyoxyethylenated straight chain alcohols, polyoxyethylenatedpolyoxypropylene glycols, polyoxyethylenated mercaptans, long chaincarboxylic acid esters (for example, glyceryl and polyglyceryl esters ofnatural fatty acids, propylene glycol, sorbitol, polyoxyethylenatedsorbitol esters, polyoxyethylene glycol esters, etc.) and alkanolamines(e.g., diethanolamine-fatty acid condensates and isopropanolamine-fattyacid condensates). In some cases, an apparatus for creating asingle-cell sequencing library via a microfluidic system provides forvolume-driven flow, wherein constant volumes are injected over time. Thepressure in fluidic channels is a function of injection rate and channeldimensions. In one embodiment, the device provides an oil/surfactantinlet; an inlet for an analyte; a filter, an inlet for mRNA capturemicrobeads and lysis reagent; a carrier fluid channel which connects theinlets; a resistor; a constriction for droplet pinch-off; a mixer; andan outlet for drops. In an embodiment the invention provides apparatusfor creating a single-cell sequencing library via a microfluidic system,which may comprise: an oil-surfactant inlet which may comprise a filterand a carrier fluid channel, wherein said carrier fluid channel mayfurther comprise a resistor; an inlet for an analyte which may comprisea filter and a carrier fluid channel, wherein said carrier fluid channelmay further comprise a resistor; an inlet for mRNA capture microbeadsand lysis reagent which may comprise a filter and a carrier fluidchannel, wherein said carrier fluid channel further may comprise aresistor; said carrier fluid channels have a carrier fluid flowingtherein at an adjustable or predetermined flow rate; wherein each saidcarrier fluid channels merge at a junction; and said junction beingconnected to a mixer, which contains an outlet for drops. Accordingly,an apparatus for creating a single-cell sequencing library via amicrofluidic system icrofluidic flow scheme for single-cell RNA-seq isenvisioned. Two channels, one carrying cell suspensions, and the othercarrying uniquely barcoded mRNA capture bead, lysis buffer and librarypreparation reagents meet at a junction and is immediatelyco-encapsulated in an inert carrier oil, at the rate of one cell and onebead per drop. In each drop, using the bead's barcode taggedoligonucleotides as cDNA template, each mRNA is tagged with a unique,cell-specific identifier. The invention also encompasses use of aDrop-Seq library of a mixture of mouse and human cells. The carrierfluid may be caused to flow through the outlet channel so that thesurfactant in the carrier fluid coats the channel walls. Thefluorosurfactant can be prepared by reacting the perflourinatedpolyether DuPont Krytox 157 FSL, FSM, or FSH with aqueous ammoniumhydroxide in a volatile fluorinated solvent. The solvent and residualwater and ammonia can be removed with a rotary evaporator. Thesurfactant can then be dissolved (e.g., 2.5 wt %) in a fluorinated oil(e.g., Flourinert (3M)), which then serves as the carrier fluid.Activation of sample fluid reservoirs to produce regent droplets isbased on the concept of dynamic reagent delivery (e.g., combinatorialbarcoding) via an on demand capability. The on demand feature may beprovided by one of a variety of technical capabilities for releasingdelivery droplets to a primary droplet, as described herein. From thisdisclosure and herein cited documents and knowledge in the art, it iswithin the ambit of the skilled person to develop flow rates, channellengths, and channel geometries; and establish droplets containingrandom or specified reagent combinations can be generated on demand andmerged with the “reaction chamber” droplets containing thesamples/cells/substrates of interest. By incorporating a plurality ofunique tags into the additional droplets and joining the tags to a solidsupport designed to be specific to the primary droplet, the conditionsthat the primary droplet is exposed to may be encoded and recorded. Forexample, nucleic acid tags can be sequentially ligated to create asequence reflecting conditions and order of same. Alternatively, thetags can be added independently appended to solid support. Non-limitingexamples of a dynamic labeling system that may be used tobioninformatically record information can be found at US ProvisionalPatent Application entitled “Compositions and Methods for UniqueLabeling of Agents” filed Sep. 21, 2012 and Nov. 29, 2012. In this way,two or more droplets may be exposed to a variety of differentconditions, where each time a droplet is exposed to a condition, anucleic acid encoding the condition is added to the droplet each ligatedtogether or to a unique solid support associated with the droplet suchthat, even if the droplets with different histories are later combined,the conditions of each of the droplets are remain available through thedifferent nucleic acids. Non-limiting examples of methods to evaluateresponse to exposure to a plurality of conditions can be found at USProvisional Patent Application entitled “Systems and Methods for DropletTagging” filed Sep. 21, 2012. Accordingly, in or as to the invention itis envisioned that there can be the dynamic generation of molecularbarcodes (e.g., DNA oligonucleotides, flurophores, etc.) eitherindependent from or in concert with the controlled delivery of variouscompounds of interest (drugs, small molecules, siRNA, CRISPR guide RNAs,reagents, etc.). For example, unique molecular barcodes can be createdin one array of nozzles while individual compounds or combinations ofcompounds can be generated by another nozzle array. Barcodes/compoundsof interest can then be merged with cell-containing droplets. Anelectronic record in the form of a computer log file is kept toassociate the barcode delivered with the downstream reagent(s)delivered. This methodology makes it possible to efficiently screen alarge population of cells for applications such as single-cell drugscreening, controlled perturbation of regulatory pathways, etc. Thedevice and techniques of the disclosed invention facilitate efforts toperform studies that require data resolution at the single cell (orsingle molecule) level and in a cost effective manner. The inventionenvisions a high throughput and high resolution delivery of reagents toindividual emulsion droplets that may contain cells, nucleic acids,proteins, etc. through the use of monodisperse aqueous droplets that aregenerated one by one in a microfluidic chip as a water-in-oil emulsion.Being able to dynamically track individual cells and droplettreatments/combinations during life cycle experiments, and having anability to create a library of emulsion droplets on demand with thefurther capability of manipulating the droplets through the disclosedprocess(es) are advantagous. In the practice of the invention there canbe dynamic tracking of the droplets and create a history of dropletdeployment and application in a single cell based environment. Dropletgeneration and deployment is produced via a dynamic indexing strategyand in a controlled fashion in accordance with disclosed embodiments ofthe present invention. Microdroplets can be processed, analyzed andsorted at a highly efficient rate of several thousand droplets persecond, providing a powerful platform which allows rapid screening ofmillions of distinct compounds, biological probes, proteins or cellseither in cellular models of biological mechanisms of disease, or inbiochemical, or pharmacological assays. A plurality of biological assaysas well as biological synthesis are contemplated. Polymerase chainreactions (PCR) are contemplated (see, e.g., US Patent Publication No.20120219947). Methods of the invention may be used for merging samplefluids for conducting any type of chemical reaction or any type ofbiological assay. There may be merging sample fluids for conducting anamplification reaction in a droplet. Amplification refers to productionof additional copies of a nucleic acid sequence and is generally carriedout using polymerase chain reaction or other technologies well known inthe art (e.g., Dieffenbach and Dveksler, PCR Primer, a LaboratoryManual, Cold Spring Harbor Press, Plainview, N.Y. [1995]). Theamplification reaction may be any amplification reaction known in theart that amplifies nucleic acid molecules, such as polymerase chainreaction, nested polymerase chain reaction, polymerase chainreaction-single strand conformation polymorphism, ligase chain reaction(Barany F. (1991) PNAS 88:189-193; Barany F. (1991) PCR Methods andApplications 1:5-16), ligase detection reaction (Barany F. (1991) PNAS88:189-193), strand displacement amplification and restriction fragmentslength polymorphism, transcription based amplification system, nucleicacid sequence-based amplification, rolling circle amplification, andhyper-branched rolling circle amplification. In certain embodiments, theamplification reaction is the polymerase chain reaction. Polymerasechain reaction (PCR) refers to methods by K. B. Mullis (U.S. Pat. Nos.4,683,195 and 4,683,202, hereby incorporated by reference) forincreasing concentration of a segment of a target sequence in a mixtureof genomic DNA without cloning or purification. The process foramplifying the target sequence includes introducing an excess ofoligonucleotide primers to a DNA mixture containing a desired targetsequence, followed by a precise sequence of thermal cycling in thepresence of a DNA polymerase. The primers are complementary to theirrespective strands of the double stranded target sequence. To effectamplification, primers are annealed to their complementary sequencewithin the target molecule. Following annealing, the primers areextended with a polymerase so as to form a new pair of complementarystrands. The steps of denaturation, primer annealing and polymeraseextension may be repeated many times (i.e., denaturation, annealing andextension constitute one cycle; there may be numerous cycles) to obtaina high concentration of an amplified segment of a desired targetsequence. The length of the amplified segment of the desired targetsequence is determined by relative positions of the primers with respectto each other, and therefore, this length is a controllable parameter.Methods for performing PCR in droplets are shown for example in Link etal. (U.S. Patent application numbers 2008/0014589, 2008/0003142, and2010/0137163), Anderson et al. (U.S. Pat. No. 7,041,481 and whichreissued as RE41,780) and European publication number EP2047910 toRaindance Technologies Inc. The content of each of which is incorporatedby reference herein in its entirety. The first sample fluid containsnucleic acid templates. Droplets of the first sample fluid are formed asdescribed above. Those droplets will include the nucleic acid templates.In certain embodiments, the droplets will include only a single nucleicacid template, and thus digital PCR may be conducted. The second samplefluid contains reagents for the PCR reaction. Such reagents generallyinclude Taq polymerase, deoxynucleotides of type A, C, G and T,magnesium chloride, and forward and reverse primers, all suspendedwithin an aqueous buffer. The second fluid also includes detectablylabeled probes for detection of the amplified target nucleic acid, thedetails of which are discussed below. This type of partitioning of thereagents between the two sample fluids is not the only possibility. Insome instances, the first sample fluid will include some or all of thereagents necessary for the PCR whereas the second sample fluid willcontain the balance of the reagents necessary for the PCR together withthe detection probes. Primers may be prepared by a variety of methodsincluding but not limited to cloning of appropriate sequences and directchemical synthesis using methods well known in the art (Narang et al.,Methods Enzymol., 68:90 (1979); Brown et al., Methods Enzymol., 68:109(1979)). Primers may also be obtained from commercial sources such asOperon Technologies, Amersham Pharmacia Biotech, Sigma, and LifeTechnologies. The primers may have an identical melting temperature. Thelengths of the primers may be extended or shortened at the 5′ end or the3′ end to produce primers with desired melting temperatures. Also, theannealing position of each primer pair may be designed such that thesequence and, length of the primer pairs yield the desired meltingtemperature. The simplest equation for determining the meltingtemperature of primers smaller than 25 base pairs is the Wallace Rule(Td=2(A+T)+4(G+C)). Computer programs may also be used to designprimers, including but not limited to Array Designer Software (ArrayitInc.), Oligonucleotide Probe Sequence Design Software for GeneticAnalysis (Olympus Optical Co.), NetPrimer, and DNAsis from HitachiSoftware Engineering. The TM (melting or annealing temperature) of eachprimer is calculated using software programs such as Oligo Design,available from Invitrogen Corp.

A droplet containing the nucleic acid is then caused to merge with thePCR reagents in the second fluid according to methods of the inventiondescribed above, producing a droplet that includes Taq polymerase,deoxynucleotides of type A, C, G and T, magnesium chloride, forward andreverse primers, detectably labeled probes, and the target nucleic acid.Once mixed droplets have been produced, the droplets are thermal cycled,resulting in amplification of the target nucleic acid in each droplet.Droplets may be flowed through a channel in a serpentine path betweenheating and cooling lines to amplify the nucleic acid in the droplet.The width and depth of the channel may be adjusted to set the residencetime at each temperature, which may be controlled to anywhere betweenless than a second and minutes. The three temperature zones may be usedfor the amplification reaction. The three temperature zones arecontrolled to result in denaturation of double stranded nucleic acid(high temperature zone), annealing of primers (low temperature zones),and amplification of single stranded nucleic acid to produce doublestranded nucleic acids (intermediate temperature zones). Thetemperatures within these zones fall within ranges well known in the artfor conducting PCR reactions. See for example, Sambrook et al.(Molecular Cloning, A Laboratory Manual, 3rd edition, Cold Spring HarborLaboratory Press, Cold Spring Harbor, N.Y., 2001). The three temperaturezones can be controlled to have temperatures as follows: 95° C. (TH),55° C. (TL), 72° C. (TM). The prepared sample droplets flow through thechannel at a controlled rate. The sample droplets first pass the initialdenaturation zone (TH) before thermal cycling. The initial preheat is anextended zone to ensure that nucleic acids within the sample droplethave denatured successfully before thermal cycling. The requirement fora preheat zone and the length of denaturation time required is dependenton the chemistry being used in the reaction. The samples pass into thehigh temperature zone, of approximately 95° C., where the sample isfirst separated into single stranded DNA in a process calleddenaturation. The sample then flows to the low temperature, ofapproximately 55° C., where the hybridization process takes place,during which the primers anneal to the complementary sequences of thesample. Finally, as the sample flows through the third mediumtemperature, of approximately 72° C., the polymerase process occurs whenthe primers are extended along the single strand of DNA with athermostable enzyme. The nucleic acids undergo the same thermal cyclingand chemical reaction as the droplets pass through each thermal cycle asthey flow through the channel. The total number of cycles in the deviceis easily altered by an extension of thermal zones. The sample undergoesthe same thermal cycling and chemical reaction as it passes through Namplification cycles of the complete thermal device. In other aspects,the temperature zones are controlled to achieve two individualtemperature zones for a PCR reaction. In certain embodiments, the twotemperature zones are controlled to have temperatures as follows: 95° C.(TH) and 60° C. (TL). The sample droplet optionally flows through aninitial preheat zone before entering thermal cycling. The preheat zonemay be important for some chemistry for activation and also to ensurethat double stranded nucleic acid in the droplets is fully denaturedbefore the thermal cycling reaction begins. In an exemplary embodiment,the preheat dwell length results in approximately 10 minutes preheat ofthe droplets at the higher temperature. The sample droplet continuesinto the high temperature zone, of approximately 95° C., where thesample is first separated into single stranded DNA in a process calleddenaturation. The sample then flows through the device to the lowtemperature zone, of approximately 60° C., where the hybridizationprocess takes place, during which the primers anneal to thecomplementary sequences of the sample. Finally the polymerase processoccurs when the primers are extended along the single strand of DNA witha thermostable enzyme. The sample undergoes the same thermal cycling andchemical reaction as it passes through each thermal cycle of thecomplete device. The total number of cycles in the device is easilyaltered by an extension of block length and tubing. After amplification,droplets may be flowed to a detection module for detection ofamplification products. The droplets may be individually analyzed anddetected using any methods known in the art, such as detecting for thepresence or amount of a reporter. Generally, a detection module is incommunication with one or more detection apparatuses. Detectionapparatuses may be optical or electrical detectors or combinationsthereof. Examples of suitable detection apparatuses include opticalwaveguides, microscopes, diodes, light stimulating devices, (e.g.,lasers), photo multiplier tubes, and processors (e.g., computers andsoftware), and combinations thereof, which cooperate to detect a signalrepresentative of a characteristic, marker, or reporter, and todetermine and direct the measurement or the sorting action at a sortingmodule. Further description of detection modules and methods ofdetecting amplification products in droplets are shown in Link et al.(U.S. patent application numbers 2008/0014589, 2008/0003142, and2010/0137163) and European publication number EP2047910 to RaindanceTechnologies Inc.

Examples of assays are also ELISA assays (see, e.g., US PatentPublication No. 20100022414). The present invention provides anotheremulsion library which may comprise a plurality of aqueous dropletswithin an immiscible fluorocarbon oil which may comprise at least onefluorosurfactant, wherein each droplet is uniform in size and maycomprise at least a first antibody, and a single element linked to atleast a second antibody, wherein said first and second antibodies aredifferent. In one example, each library element may comprise a differentbead, wherein each bead is attached to a number of antibodies and thebead is encapsulated within a droplet that contains a different antibodyin solution. These antibodies may then be allowed to form “ELISAsandwiches,” which may be washed and prepared for a ELISA assay.Further, these contents of the droplets may be altered to be specificfor the antibody contained therein to maximize the results of the assay.Single-cell assays are also contemplated as part of the presentinvention (see, e.g., Ryan et al., Biomicrofluidics 5, 021501 (2011) foran overview of applications of microfluidics to assay individual cells).A single-cell assay may be contemplated as an experiment that quantifiesa function or property of an individual cell when the interactions ofthat cell with its environment may be controlled precisely or may beisolated from the function or property under examination. The researchand development of single-cell assays is largely predicated on thenotion that genetic variation causes disease and that smallsubpopulations of cells represent the origin of the disease. Methods ofassaying compounds secreted from cells, subcellular components,cell-cell or cell-drug interactions as well as methods of patterningindividual cells are also contemplated within the present invention.

These and other technologies may be employed in or as to the practice ofthe instant invention.

Example 1: Materials and Methods

Briefly, gene expression profiles were measured at 18 time points (0.5hr to 72 days) under Th17 conditions (IL-6, TGF-β1) or control (Th0)using Affymetrix microarrays HT_MG-430A. Differentially expressed geneswere detected using a consensus over four inference methods, and clusterthe genes using k-means, with an automatically derived k. Temporalregulatory interactions were inferred by looking for significant(p<5*10⁻⁵ and fold enrichment>1.5) overlaps between the regulator'sputative targets (e.g., based on ChIPseq) and the target gene's cluster(using four clustering schemes). Candidates for perturbation wereordered lexicographically using network-based and expression-basedfeatures. Perturbations were done using SiNW for siRNA delivery. Thesemethods are described in more detail below.

Mice:

C57BL/6 wild-type (wt), Mt^(−/−), Irf1^(−/−), Fas^(−/−), Irf4^(fl/fl),and Cd4^(Cre) mice were obtained from Jackson Laboratory (Bar Harbor,Me.). Stat1^(−/−) and 129/Sv control mice were purchased from Taconic(Hudson, N.Y.). IL-12rβ1^(−/−) mice were provided by Dr. Pahan Kalipadafrom Rush University Medical Center. IL-17Ra^(−/−) mice were provided byDr. Jay Kolls from Louisiana State University/University of Pittsburgh.Irf8^(fl/fl) mice were provided by Dr. Keiko Ozato from the NationalInstitute of Health. Both Irf4^(fl/fl) and Irf8^(fl/fl) mice werecrossed to Cd4^(Cre) mice to generate Cd4^(Cre)xIrf4^(fl/fl) andCd4^(Cre)xIrf8^(fl/fl) mice. All animals were housed and maintained in aconventional pathogen-free facility at the Harvard Institute of Medicinein Boston, Mass. (IUCAC protocols: 0311-031-14 (VKK) and 0609-058015(AR)). All experiments were performed in accordance to the guidelinesoutlined by the Harvard Medical Area Standing Committee on Animals atthe Harvard Medical School (Boston, Mass.). In addition, spleensfromMina^(−/−) mice were provided by Dr. Mark Bix from St. JudeChildren's Research Hospital (IACUC Protocol: 453). Pou2af1^(−/−) micewere obtained from the laboratory of Dr. Robert Roeder (Kim, U. et al.The B-cell-specific transcription coactivator OCA-B/OBF-1/Bob-1 isessential for normal production of immunoglobulin isotypes. Nature 383,542-547, doi:10.1038/383542a0 (1996)). Wild-type and Oct1^(−/−) fetallivers were obtained at day E12.5 and transplanted into sub-lethallyirradiated Rag1^(−/−) mice as previously described (Wang, V. E., Tantin,D., Chen, J. & Sharp, P. A. B cell development and immunoglobulintranscription in Oct-1-deficient mice. Proc. Natl. Acad. Sci. U.S.A.101, 2005-2010, doi:10.1073/pnas.0307304101 (2004)) (IACUC Protocol:11-09003).

Cell Sorting and In Vitro T-Cell Differentiation in Petri Dishes:

Cd4+ T cells were purified from spleen and lymph nodes using anti-CD4microbeads (Miltenyi Biotech) then stained in PBS with 1% FCS for 20 minat room temperature with anti-Cd4-PerCP, anti-Cd62l-APC, andanti-Cd44-PE antibodies (all Biolegend, CA).

Naïve Cd4⁺ Cd62l^(high) Cd44^(low) T cells were sorted using the BDFACSAria cell sorter. Sorted cells were activated with plate boundanti-Cd3 (2 μg/ml) and anti-Cd28 (2 μg/ml) in the presence of cytokines.For Th17 differentiation: 2 ng/mL rhTGF-β1 (Miltenyi Biotec), 25 ng/mLrmIl-6 (Miltenyi Biotec), 20 ng/ml rmIl-23 (Miltenyi Biotec), and 20ng/ml rmIL-β1 (Miltenyi Biotec). Cells were cultured for 0.5-72 hoursand harvested for RNA, intracellular cytokine staining, and flowcytometry.

Flow Cytometry and Intracellular Cytokine Staining (ICC):

Sorted naïve T cells were stimulated with phorbol 12-myristate 13-aceate(PMA) (50 ng/ml, Sigma-aldrich, MO), ionomycin (1 μg/ml, Sigma-aldrich,MO) and a protein transport inhibitor containing monensin (Golgistop)(BD Biosciences) for four hours prior to detection by staining withantibodies. Surface markers were stained in PBS with 1% FCS for 20 minat room temperature, then subsequently the cells were fixed inCytoperm/Cytofix (BD Biosciences), permeabilized with Perm/Wash Buffer(BD Biosciences) and stained with Biolegend conjugated antibodies, i.e.Brilliant Violet 650™ anti-mouse IFN-γ (XMG1.2) andallophycocyanin-anti-IL-17A (TC11-18H10.1), diluted in Perm/Wash bufferas described (Bettelli, E. et al. Reciprocal developmental pathways forthe generation of pathogenic effector TH17 and regulatory T cells.Nature 441, 235-238 (2006)) (FIG. 5, FIG. 16). To measure thetime-course of RORγt protein expression, a phycoerythrin-conjugatedanti-Retinoid-Related Orphan Receptor gamma was used (B2D), also fromeBioscience (FIG. 16). FOXP3 staining for cells from knockout mice wasperformed with the FOXP3 staining kit by eBioscience (00-5523-00) inaccordance with their “Onestep protocol for intracellular (nuclear)proteins”. Data was collected using either a FACS Calibur or LSR II(Both BD Biosciences), then analyzed using Flow Jo software (Treestar)(Awasthi, A. et al. A dominant function for interleukin 27 in generatinginterleukin 10-producing anti-inflammatory T cells. Nature immunology 8,1380-1389, doi:10.1038/ni1541 (2007); Awasthi, A. et al. Cutting edge:IL-23 receptor gfp reporter mice reveal distinct populations ofIL-17-producing cells. J Immunol 182, 5904-5908,doi:10.4049/jimmunol.0900732 (2009)).

Quantification of Cytokine Secretion Using Enzyme-Linked ImmunosorbentAssay (ELISA):

Naïve T cells from knockout mice and their wild type controls werecultured as described above, their supernatants were collected after 72h, and cytokine concentrations were determined by ELISA (antibodies forIL-17 and IL-10 from BD Bioscience) or by cytometric bead array for theindicated cytokines (BD Bioscience), according to the manufacturers'instructions (FIG. 5, FIG. 16).

Microarray Data:

Naïve T cells were isolated from WT mice, and treated with IL-6 andTGF-β1. Affymetrix microarrays HT_MG-430A were used to measure theresulting mRNA levels at 18 different time points (0.5-72 h; FIG. 1b ).In addition, cells treated initially with IL-6, TGF-β1 and with additionof IL-23 after 48 hr were profiled at five time points (50-72 h). Ascontrol, time- and culture-matched WT naïve T cells stimulated under Th0conditions were used. Biological replicates were measured in eight ofthe eighteen time points (1 hr, 2 hr, 10 hr, 20 hr, 30 hr, 42 hr, 52 hr,60 hr) with high reproducibility (r²>0.98). For further validation, thedifferentiation time course was compared to published microarray data ofTh17 cells and naïve T cells (Wei, G. et al. in Immunity Vol. 30 155-167(2009)) (FIG. 6c ). In an additional dataset naïve T cells were isolatedfrom WT and Il23r^(−/−) mice, and treated with IL-6, TGF-β1 and IL-23and profiled at four different time points (49 hr, 54 hr, 65 hr, 72 hr).Expression data was preprocessed using the RMA algorithm followed byquantile normalization (Reich, M. et al. GenePattern 2.0. Naturegenetics 38, 500-501, doi:10.1038/ng0506-500 (2006)).

Detecting Differentially Expressed Genes:

Differentially expressed genes (comparing to the Th0 control) were foundusing four methods: (1) Fold change. Requiring a 2-fold change (up ordown) during at least two time points. (2) Polynomial fit. The EDGEsoftware (Storey, J., Xiao, W., Leek, J., Tompkins, R. & Davis, R. inProc. Natl. Acad. Sci. U.S.A. vol. 102 12837 (2005); Leek, J. T.,Monsen, E., Dabney, A. R. & Storey, J. D. EDGE: extraction and analysisof differential gene expression. Bioinformatics 22, 507-508,doi:10.1093/bioinformatics/btk005 (2006)), designed to identifydifferential expression in time course data, was used with a thresholdof q-value≤0.01. (3) Sigmoidal fit. An algorithm similar to EDGE whilereplacing the polynomials with a sigmoid function, which is often moreadequate for modeling time course gene expression data (Chechik, G. &Koller, D. Timing of gene expression responses to environmental changes.J Comput Biol 16, 279-290,doi:10.1089/cmb.2008.13TT10.1089/cmb.2008.13TT [pii] (2009)), was used.A threshold of q-value≤0.01. (4) ANOVA was used. Gene expression ismodeled by: time (using only time points for which there was more thanone replicate) and treatment (“TGF-β1+IL-6” or “Th0”). The model takesinto account each variable independently, as well as their interaction.Cases in which the p-value assigned with the treatment parameter or theinteraction parameter passed an FDR threshold of 0.01 were reported.

Overall, substantial overlap between the methods (average of 82% betweenany pair of methods) observed. The differential expression score of agene was defined as the number of tests that detected it. Asdifferentially expressed genes, cases with differential expressionscore>3 were reported.

For the Il23r^(−/−) time course (compared to the WT T cells) methods 1.3(above) were used. Here, a fold change cutoff of 1.5 was used, and genesdetected by at least two tests were reported.

Clustering:

several ways for grouping the differentially expressed genes wereconsidered, based on their time course expression data: (1) For eachtime point, two groups were defined: (a) all the genes that areover-expressed and (b) all the genes that are under-expressed relativeto Th0 cells (see below); (2) For each time point, two groups weredefined: (a) all the genes that are induced and (b) all the genes thatare repressed, comparing to the previous time point; (3) K-meansclustering using only the Th17 polarizing conditions. The minimal k wasused, such that the within-cluster similarity (average Pearsoncorrelation with the cluster's centroid) was higher than 0.75 for allclusters; and, (4) K-means clustering using a concatenation of the Th0and Th17 profiles.

For methods (1, 2), to decide whether to include a gene, its originalmRNA expression profiles (Th0, Th17), and their approximations assigmoidal functions (Chechik, G. & Koller, D. Timing of gene expressionresponses to environmental changes. J Comput Biol 16, 279-290,doi:10.1089/cmb.2008.13TT10.1089/cmb.2008.13TT [pii] (2009)) (thusfiltering transient fluctuations) were considered. The fold changelevels (compared to Th0 (method 1) or to the previous time point (method2)) were required to pass a cutoff defined as the minimum of thefollowing three values: (1) 1.7; (2) mean+std of the histogram of foldchanges across all time points; or (3) the maximum fold change acrossall time points. The clusters presented in FIG. 1b were obtained withmethod 4.

Regulatory Network Inference:

potential regulators of Th17 differentiation were identified bycomputing overlaps between their putative targets and sets ofdifferentially expressed genes grouped according to methods 1-4 above.regulator-target associations from several sources were assembled: (1)in vivo DNA binding profiles (typically measured in other cells) of 298transcriptional regulators (Linhart, C., Halperin, Y. & Shamir, R.Transcription factor and microRNA motif discovery: the Amadeus platformand a compendium of metazoan target sets. Genome research 18, 1180-1189,doi:10.1101/gr.076117.108 (2008); Zheng, G. et al. ITFP: an integratedplatform of mammalian transcription factors. Bioinformatics 24,2416-2417, doi:10.1093/bioinformatics/btn439 (2008); Wilson, N. K. etal. Combinatorial transcriptional control in blood stem/progenitorcells: genome-wide analysis of ten major transcriptional regulators.Cell Stem Cell 7, 532-544, doi:S1934-5909(10)00440-6[pii]10.1016/j.stem.2010.07.016 (2010); Lachmann, A. et al. inBioinformatics Vol. 26 2438-2444 (2010); Liberzon, A. et al. Molecularsignatures database (MSigDB) 3.0. Bioinformatics 27, 1739-1740,doi:10.1093/bioinformatics/btr260 (2011); Jiang, C., Xuan, Z., Zhao, F.& Zhang, M. TRED: a transcriptional regulatory element database, newentries and other development. Nucleic Acids Res 35, D137-140 (2007));(2) transcriptional responses to the knockout of 11 regulatory proteins(Awasthi et al., J. Immunol 2009; Schraml, B. U. et al. The AP-1transcription factor Batf controls T(H)17 differentiation. Nature 460,405-409, doi:nature08114 [pii]10.1038/nature08114 (2009); Shi, L. Z. etal. HIF1alpha-dependent glycolytic pathway orchestrates a metaboliccheckpoint for the differentiation of TH17 and Treg cells. The Journalof experimental medicine 208, 1367-1376, doi:10.1084/jem.20110278(2011); Yang, X. P. et al. Opposing regulation of the locus encodingIL-17 through direct, reciprocal actions of STAT3 and STAT5. Natureimmunology 12, 247-254, doi:10.1038/ni.1995 (2011); Durant, L. et al.Diverse Targets of the Transcription Factor STAT3 Contribute to T CellPathogenicity and Homeostasis. Immunity 32, 605-615,doi:10.1016/j.immuni.2010.05.003 (2010); Jux, B., Kadow, S. & Esser, C.Langerhans cell maturation and contact hypersensitivity are impaired inaryl hydrocarbon receptor-null mice. Journal of immunology (Baltimore,Md.: 1950) 182, 6709-6717, doi:10.4049/jimmunol.0713344 (2009); Amit, I.et al. Unbiased reconstruction of a mammalian transcriptional networkmediating pathogen responses. Science 326, 257-263,doi:10.1126/science.1179050 (2009); Xiao, S. et al. Retinoic acidincreases Foxp3+ regulatory T cells and inhibits development of Th17cells by enhancing TGF-beta-driven Smad3 signaling and inhibiting IL-6and IL-23 receptor expression. J Immunol 181, 2277-2284, doi:181/4/2277[pii] (2008)); (3) additional potential interactions obtained byapplying the Ontogenet algorithm (Jojic et al., under review; regulatorymodel available at: to data from the mouse ImmGen consortium (January2010 release (Heng, T. S. & Painter, M. W. The Immunological GenomeProject: networks of gene expression in immune cells. Nature immunology9, 1091-1094, doi:10.1038/ni1008-1091 (2008)), which includes 484microarray samples from 159 cell subsets from the innate and adaptiveimmune system of mice; (4) a statistical analysis of cis-regulatoryelement enrichment in promoter regions (Elkon, R., Linhart, C., Sharan,R., Shamir, R. & Shiloh, Y. in Genome Research Vol. 13 773-780 (2003);Odabasioglu, A., Celik, M. & Pileggi, L. T. in Proceedings of the 1997IEEE/ACM international conference on Computer-aided design 58-65 (IEEEComputer Society, San Jose, Calif., United States, 1997)); and, (5) theTF enrichment module of the IPA software. For every TF in the database,the statistical significance of the overlap between its putative targetsand each of the groups defined above using a Fisher's exact test wascomputed. Cases where p<5*10⁻⁵ and the fold enrichment>1.5 wereincluded.

Each edge in the regulatory network was assigned a time stamp based onthe expression profiles of its respective regulator and target nodes.For the target node, the time points at which a gene was eitherdifferentially expressed or significantly induced or repressed withrespect to the previous time point (similarly to grouping methods 1 and2 above) were considered. A regulator node was defined as ‘absent’ at agiven time point if: (i) it was under expressed compared to Th0; or (ii)the expression is low (<20% of the maximum value in time) and the genewas not over-expressed compared to Th0; or, (iii) up to this point intime the gene was not expressed above a minimal expression value of 100.As an additional constraint, protein expression levels were estimatedusing the model from Schwanhäusser, B. et al. (Global quantification ofmammalian gene expression control. Nature 473, 337-342,doi:10.1038/nature10098 (2011)) and using a sigmoidal fit (Chechik &Koller, J Comput Biol 2009) for a continuous representation of thetemporal expression profiles, and the ProtParam software (Wilkins, M. R.et al. Protein identification and analysis tools in the ExPASy server.Methods Mol. Biol. 112, 531-552 (1999)) for estimating proteinhalf-lives. It was required that, in a given time point, the predictedprotein level be no less than 1.7 fold below the maximum value attainedduring the time course, and not be less than 1.7 fold below the Th0levels. The timing assigned to edges inferred based on a time-pointspecific grouping (grouping methods 1 and 2 above) was limited to thatspecific time point. For instance, if an edge was inferred based onenrichment in the set of genes induced at 1 hr (grouping method #2), itwill be assigned a “1 hr” time stamp. This same edge could then onlyhave additional time stamps if it was revealed by additional tests.

Selection of Nanostring Signature Genes:

The selection of the 275-gene signature (Table 1) combined severalcriteria to reflect as many aspect of the differentiation program as waspossible. The following requirements were defined: (1) the signaturemust include all of the TFs that belong to a Th17 microarray signature(comparing to other CD4+ T cells (Wei et al., in Immunity vol. 30155-167 (2009)), see Methods described herein); that are included asregulators in the network and have a differential expression score>1; orthat are strongly differentially expressed (differential expressionscore=4); (2) it must include at least 10 representatives from eachcluster of genes that have similar expression profiles (using clusteringmethod (4) above); (3) it must contain at least 5 representatives fromthe predicted targets of each TF in the different networks; (4) it mustinclude a minimal number of representatives from each enriched GeneOntology (GO) category (computed across all differentially expressedgenes); and, (5) it must include a manually assembled list of ˜100 genesthat are related to the differentiation process, including thedifferentially expressed cytokines, receptor molecules and other cellsurface molecules. Since these different criteria might generatesubstantial overlaps, a set-cover algorithm was used to find thesmallest subset of genes that satisfies all of five conditions. To thislist 18 genes whose expression showed no change (in time or betweentreatments) in the microarray data were added.

The 85-gene signature (used for the Fluidigm BioMark qPCR assay) is asubset of the 275-gene signature, selected to include all the keyregulators and cytokines discussed. To this list 10 control genes(2900064A13RIK, API5, CAND1, CSNK1A1, EIF3E, EIF3H, FIP1L1, GOLGA3,HSBP1, KHDRBS1, MED24, MKLN1, PCBP2, SLC6A6, SUFU, TMED7, UBE3A, ZFP410)were added.

Selection of Perturbation Targets:

an unbiased approach was used to rank candidate regulators—transcriptionfactor or chromatin modifier genes—of Th17 differentiation. The rankingwas based on the following features: (a) whether the gene encoding theregulator belonged to the Th17 microarray signature (comparing to otherCD4+ T cells (Wei et al., in Immunity vol. 30 155-167 (2009)), seeMethods described herein); (b) whether the regulator was predicted totarget key Th17 molecules (IL-17, IL-21, IL23r, and ROR-γt); (c) whetherthe regulator was detected based on both perturbation and physicalbinding data from the IPA software; (d) whether the regulator wasincluded in the network using a cutoff of at least 10 target genes; (e)whether the gene encoding for the regulator was significantly induced inthe Th17 time course. Only cases where the induction happened after 4hours were considered to exclude non-specific hits; (f) whether the geneencoding the regulator was differentially expressed in response toTh17-related perturbations in previous studies. For this criterion, adatabase of transcriptional effects in perturbed Th17 cells wasassembled, including: knockouts of Batf (Schraml et al., Nature 2009),ROR-γt (Xiao et al., unpublished), Hif1a (Shi et al., J. Exp. Med.(2011)), Stat3 and Stat5 (Yang et al., Nature Immunol (2011); Durant, L.et al. in Immunity Vol. 32 605-615 (2010), Tbx21 (Awasthi et al.,unpublished), IL23r (this study), and Ahr (Jux et al., J. Immunol2009)). Data from the Th17 response to Digoxin (Huh, J. R. et al.Digoxin and its derivatives suppress TH17 cell differentiation byantagonizing RORgammat activity. Nature 472, 486-490,doi:10.1038/nature09978 (2011)) and Halofuginone (Sundrud, M. S. et al.Halofuginone inhibits TH17 cell differentiation by activating the aminoacid starvation response. Science (New York, N.Y.) 324, 1334-1338,doi:10.1126/science.1172638 (2009)), as well as information on directbinding by ROR-γt as inferred from ChIP-seq data (Xiao et al.,unpublished) was also included. The analysis of the published expressiondata sets is described in the Methods described herein. For eachregulator, the number of conditions in which it came up as a significanthit (up/down-regulated or bound) was counted; for regulators with 2 to 3hits (quantiles 3 to 7 out of 10 bins), a score of 1 was then assign;for regulators with more than 3 hits (quantiles 8-10), a score of 2 (ascore of 0 is assigned otherwise) was assigned; and, (g) thedifferential expression score of the gene in the Th17 time course.

The regulators were ordered lexicographically by the above featuresaccording to the order: a, b, c, d, (sum of e and f), g—that is, firstsort according to a then break ties according to b, and so on. Genesthat are not over-expressed during at least one time point wereexcluded. As an exception, predicted regulators (feature d) that hadadditional external validation (feature f) were retained. To validatethis ranking, a supervised test was used: 74 regulators that werepreviously associated with Th17 differentiation were manually annotated.

All of the features are highly specific for these regulators (p<10⁻³).Moreover, using a supervised learning method (Naïve Bayes), the featuresprovided good predictive ability for the annotated regulators (accuracyof 71%, using 5-fold cross validation), and the resulting ranking washighly correlated with the unsupervised lexicographic ordering (Spearmancorrelation>0.86).

This strategy was adapted for ranking protein receptors. To this end,feature c was excluded and the remaining “protein-level” features (b andd) were replaced with the following definitions: (b) whether therespective ligand is induced during the Th17 time course; and, (d)whether the receptor was included as a target in the network using acutoff of at least 5 targeting transcriptional regulators.

Gene Knockdown Using Silicon Nanowires:

4×4 mm silicon nanowire (NW) substrates were prepared and coated with 3μL of a 50 μM pool of four siGENOME siRNAs (Dharmcon) in 96 well tissueculture plates, as previously described (Shalek, A. K. et al. Verticalsilicon nanowires as a universal platform for delivering biomoleculesinto living cells. Proceedings of the National Academy of Sciences ofthe United States of America 107, 1870-1875, doi:10.1073/pnas.0909350107(2010)). Briefly, 150,000 naïve T cells were seeded on siRNA-laced NWsin 10 μL of complete media and placed in a cell culture incubator (37°C., 5% CO₂) to settle for 45 minutes before full media addition. Thesesamples were left undisturbed for 24 hours to allow target transcriptknockdown. Afterward, siRNA-transfected T cells were activated withαCd3/Cd28 dynabeads (Invitrogen), according to the manufacturer'srecommendations, under Th17 polarization conditions (TGF-β1 & IL-6, asabove). 10 or 48 hr post-activation, culture media was removed from eachwell and samples were gently washed with 100 μL of PBS before beinglysed in 20 μL of buffer TCL (Qiagen) supplemented with2-mercaptoethanol (1:100 by volume). After mRNA was harvested inTurbocapture plates (Qiagen) and converted to cDNA using Sensiscript RTenzyme (Qiagen), qRT-PCR was used to validate both knockdown levels andphenotypic changes relative to 8-12 non-targeting siRNA control samples,as previously described (Chevrier, N. et al. Systematic discovery of TLRsignaling components delineates viral-sensing circuits. Cell 147,853-867, doi:10.1016/j.cell.2011.10.022 (2011)). A 60% reduction intarget mRNA was used as the knockdown threshold. In each knockdownexperiment, each individual siRNA pool was run in quadruplicate; eachsiRNA was tested in at least three separate experiments (FIG. 11).

mRNA Measurements in Perturbation Assays:

the nCounter system, presented in full in Geiss et al. (Geiss, G. K. etal. Direct multiplexed measurement of gene expression with color-codedprobe pairs. SI. Nature Biotechnology 26, 317-325, doi:10.1038/nbt1385(2008)), was used to measure a custom CodeSet constructed to detect atotal of 293 genes, selected as described above. The Fluidigm BioMark HDsystem was also used to measure a smaller set of 96 genes. Finally,RNA-Seq was used to follow up and validate 12 of the perturbations.

A custom CodeSet constructed to detect a total of 293 genes, selected asdescribed above, including 18 control genes whose expression remainunaffected during the time course was used. Given the scarcity of inputmRNA derived from each NW knockdown, a Nanostring-CodeSet specific, 14cycle Specific Target Amplification (STA) protocol was performedaccording to the manufacturer's recommendations by adding 5 μL of TaqManPreAmp Master Mix (Invitrogen) and 1 μL of pooled mixed primers (500 nMeach, see Table S6.1 for primer sequences) to 5 μL of cDNA from avalidated knockdown. After amplification, 5 μL of the amplified cDNAproduct was melted at 95° C. for 2 minutes, snap cooled on ice, and thenhybridized with the CodeSet at 65° C. for 16 hours. Finally, thehybridized samples were loaded into the nCounter prep station andproduct counts were quantified using the nCounter Digital Analyzerfollowing the manufacturer's instructions. Samples that were tooconcentrated after amplification were diluted and rerun. Serialdilutions (1:1, 1:4, 1:16, & 1:64, pre-STA) of whole spleen and Th17polarized cDNAs were used to both control for the effects of differentamounts of starting input material and check for biases in sampleamplification.

Nanostring nCounter Data Analysis:

For each sample, the count values were divided by the sum of counts thatwere assigned to a set of control genes that showed no change (in timeor between treatments) in the microarray data (18 genes altogether). Foreach condition, a change fold ratio was computed, comparing to at leastthree different control samples treated with non-targeting (NT) siRNAs.The results of all pairwise comparisons (i.e. A×B pairs for A repeats ofthe condition and B control (NT) samples) were then pooled together: asubstantial fold change (above a threshold value t) in the samedirection (up/down regulation) in more than half of the pairwisecomparisons was required. The threshold t was determined as max {d1,d2}, where d1 is the mean+std in the absolute log fold change betweenall pairs of matching NT samples (i.e., form the same batch and the sametime point; d1=1.66), and where d2 is the mean+1.645 times the standarddeviation in the absolute log fold change shown by the 18 control genes(determined separately for every comparison by taking all the 18×A×Bvalues; corresponding to p=0.05, under assumption of normality). Allpairwise comparisons in which both NT and knockdown samples had lowcounts before normalization (<100) were ignored.

A permutation test was used to evaluate the overlap between thepredicted network model (FIG. 2) and the knockdown effects measured inthe Nanostring nCounter (FIG. 4, FIG. 10). Two indices were computed forevery TF for which predicted target were available: (i) specificity—thepercentage of predicted targets that are affected by the respectiveknockdown (considering only genes measured by nCounter), and (ii)sensitivity—the percentage of genes affected by a given TF knockdownthat are also its predicted targets in the model. To avoid circularity,target genes predicted in the original network based on knockout alonewere excluded from this analysis. The resulting values (on average,13.5% and 24.8%, respectively) were combined into an F-score (theharmonic mean of specificity and sensitivity). The calculation ofF-score was then repeated in 500 randomized datasets, where the targetgene labels in the knockdown result matrix were shuffled. The reportedempirical p-value is:

P=(1+# randomized datasets with equal of better F-score)/(1+# randomizeddatasets)

mRNA Measurements on the Fluidigm BioMark HD:

cDNA from validated knockdowns was prepared for quantification on theFluidigm BioMark HD. Briefly, 5 μL of TaqMan PreAmp Master Mix(Invitrogen), 1 μL of pooled mixed primers (500 nM each, see Table S6.1for primers), and 1.5 μL of water were added to 2.5 μL of knockdownvalidated cDNA and 14 cycles of STA were performed according to themanufacturer's recommendations. After the STA, an Exonuclease Idigestion (New England Biosystems) was performed to removeunincorporated primers by adding 0.8 μL Exonuclease I, 0.4 μLExonuclease I Reaction Buffer and 2.8 μL water to each sample, followedby vortexing, centrifuging and heating the sample to 37° C. for 30minutes. After a 15 minute 80° C. heat inactivation, the amplifiedsample was diluted 1:5 in Buffer TE. Amplified validated knockdowns andwhole spleen and Th17 serial dilution controls (1:1, 1:4, 1:16, & 1:64,pre-STA) were then analyzed using EvaGreen and 96×96 gene expressionchips (Fluidigm BioMark HD) (Dalerba, P. et al. Single-cell dissectionof transcriptional heterogeneity in human colon tumors. Nat Biotechnol29, 1120-1127, doi:10.1038/nbt.2038 (2011)).

Fluidigm Data Analysis:

For each sample, the Ct values were subtracted from the geometric meanof the Ct values assigned to a set of four housekeeping genes. For eachcondition, a fold change ratio was computed, comparing to at least threedifferent control samples treated with non-targeting (NT) siRNAs. Theresults of all pairwise comparisons (i.e. A×B pairs for A repeats of thecondition and B control (NT) samples) were then pooled together: asubstantial difference between the normalized Ct values (above athreshold value) in the same direction (up/down regulation) in more thanhalf of the pairwise comparisons was required. The threshold t wasdetermined as max {log 2(1.5), d1(b), d2}, where d1(b) is the mean+stdin the delta between all pairs of matching NT samples (i.e., from thesame batch and the same time point), over all genes in expressionquantile b (1<=b<=10). d2 is the mean+1.645 times the standard deviationin the deltas shown by 10 control genes (the 4 housekeeping genes plus 6control genes from the Nanostring signature); d2 is determinedseparately for each comparison by taking all the 10×A×B values;corresponding to p=0.05, under assumption of normality). All pairwisecomparisons in which both NT and knockdown samples had low counts beforenormalization (Ct<21 (taking into account the amplification, this cutoffcorresponds to a conventional Ct cutoff of 35)) were ignored.

mRNA Measurements Using RNA-Seq:

Validated single stranded cDNAs from the NW-mediated knockdowns wereconverted to double stranded DNA using the NEBNext mRNA Second StrandSynthesis Module (New England BioLabs) according to the manufacturer'srecommendations. The samples were then cleaned using 0.9×SPRI beads(Beckman Coulter). Libraries were prepared using the Nextera XT DNASample Prep Kit (Illumina), quantified, pooled, and then sequenced onthe HiSeq 2500 (Illumnia) to an average depth 20M reads.

RNA-Seq Data Analysis:

a Bowtie index based on the UCSC known Gene transcriptome (Fujita, P. A.et al. The UCSC Genome Browser database: update 2011. Nucleic Acids Res.39, D876-882, doi:10.1093/nar/gkq963 (2011)) was created, and paired-endreads were aligned directly to this index using Bowtie (Langmead, B.,Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficientalignment of short DNA sequences to the human genome. Genome Biol 10,R25, doi:10.1186/gb-2009-10-3-r25 (2009)). Next, RSEM v1.11 (Li, B. &Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq datawith or without a reference genome. BMC Bioinformatics 12, 323,doi:10.1186/1471-2105-12-323 (2011)) was ran with default parameters onthese alignments to estimate expression levels. RSEM's gene levelexpression estimates (tau) were multiplied by 1,000,000 to obtaintranscript per million (TPM) estimates for each gene. Quantilenormalization was used to further normalize the TPM values within eachbatch of samples. For each condition, a fold change ratio was computed,comparing to at least two different control samples treated withnontargeting (NT) siRNAs. The results of all pairwise comparisons (i.e.A×B pairs for A repeats of the condition and B control (NT) samples)were then pooled together: a significant difference between the TPMvalues in the same direction (up/down regulation) in more than half ofthe pairwise comparisons was required. The significance cutoff t wasdetermined as max {log 2(1.5), d1(b)}, where d1(b) is the mean+1.645*stdin the log fold ratio between all pairs of matching NT samples (i.e.,from the same batch and the same time point), over all genes inexpression quantile b (1<=b<=20). All pairwise comparisons in which bothNT and knockdown samples had low counts (TPM<10) were ignored. To avoidspurious fold levels due to low expression values a small constant, setto the value of the 1st quantile (out of 10) of all TPM values in therespective batch, was add to the expression values.

A hypergeometric test was used to evaluate the overlap between thepredicted network model (FIG. 2) and the knockdown effects measured byRNA-seq (FIG. 4d ). As background, all of the genes that appeared in themicroarray data (and hence 20 have the potential to be included in thenetwork) were used. As an additional test, the Wilcoxon-Mann-Whitneyrank-sum test was used, comparing the absolute log fold-changes of genesin the annotated set to the entire set of genes (using the samebackground as before). The rank-sum test does not require setting asignificance threshold; instead, it considers the fold change values ofall the genes. The p-values produced by the rank-sum test were lower(i.e., more significant) than in the hypergeometric test, and therefore,in FIG. 4c , only the more stringent (hypergeometric) p-values werereported.

Profiling Tsc22d3 DNA Binding Using ChIP-Seq:

ChIP-seq for Tsc22d3 was performed as previously described (Ram, O. etal. Combinatorial Patterning of Chromatin Regulators Uncovered byGenome-wide Location Analysis in Human Cells. Cell 147, 1628-1639(2011)) using an antibody from Abcam. The analysis of this data wasperformed as previously described (Ram, O. et al. CombinatorialPatterning of Chromatin Regulators Uncovered by Genome-wide LocationAnalysis in Human Cells. Cell 147, 1628-1639 (2011)) and is detailed inthe Methods described herein.

Analysis of Tsc22d3 ChIP-Seq Data:

ChIP-seq reads were aligned to the NCBI Build 37 (UCSC mm9) of the mousegenome using Bowtie (Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. in Genome Biol Vol. 10 R25 (2009)). Enriched binding regions (peaks)were detected using MACS (Zhang, Y. et al. in Genome Biol Vol. 9 R137(2008)) with a pvalue cutoff of 10⁻⁸. A peak was associated with a geneif it falls in proximity to its 5′ end (10 kb upstream and 1 kbdownstream from transcription start site) or within the gene's body. TheRefSeq transcript annotations for gene's coordinates were used.

The overlap of ChIP-seq peaks with annotated genomic regions wasassessed. It was determined that a region A overlap with a peak B if Ais within a distance of 50 bp from B's summit (as determined by MACS).The regions used included: (i) regulatory features annotations from theEnsemble database (Flicek, P. et al. Ensembl 2011. Nucleic Acids Res.39, D800-806, doi:10.1093/nar/gkq1064 (2011)); (ii) regulatory 21features found by the Oregano algorithm (Smith, R. L. et al.Polymorphisms in the IL-12beta and IL-23R genes are associated withpsoriasis of early onset in a UK cohort. J Invest Dermatol 128,1325-1327, doi:5701140 [pii] 10.1038/sj.jid.5701140 (2008)); (iii)conserved regions annotated by the multiz30way algorithm (here regionswith multiz30way score>0.7 were considered); (iv) repeat regionsannotated by RepeatMasker; (v) putative promoter regions—taking 10 kbupstream and 1 kb downstream of transcripts annotated in RefSeq (Pruitt,K. D., Tatusova, T. & Maglott, D. R. NCBI reference sequences (RefSeq):a curated non-redundant sequence database of genomes, transcripts andproteins. Nucleic Acids Res. 35, D61-65, doi:10.1093/nar/gkl842 (2007));(vi) gene body annotations in RefSeq; (vii) 3′ proximal regions (taking1 kb upstream and 5 kb downstream to 3′ end); (viii) regions enriched inhistone marks H3K4me3 and H3K27me3 in Th17 cells (Wei, G. et al. inImmunity Vol. 30 155-167 (2009)); (ix) regions enriched in binding ofStat3 and Stat5 (Yang, X. P. et al. Opposing regulation of the locusencoding IL-17 through direct, reciprocal actions of STAT3 and STAT5.Nat. Immunol. 12, 247-254, doi:10.1038/ni.1995 (2011)), Irf4 and Batf(Glasmacher, E. et al. A Genomic Regulatory Element That DirectsAssembly and Function of Immune-Specific AP-1-IRF Complexes. Science,doi:10.1126/science.1228309 (2012)), and RORγt (Xiao et al unpublished)in Th17 cells, and Foxp3 in iTreg (Xiao et al., unpublished).

For each set of peaks “x” and each set of genomic regions “y”, abinomial pvalue was used to assess their overlap in the genome asdescribed in Mclean, C. Y. et al. in Nature biotechnology Vol. 28nbt.1630-1639 (2010). The number of hits is defined as the number of xpeaks that overlap with y. The background probability in sets (i)-(vii)is set to the overall length of the region (in bp) divided by theoverall length of the genome. The background probability in sets(viii)-(ix) is set to the overall length of the region divided by theoverall length of annotated genomic regions: this includes annotatedregulatory regions (as defined in sets i, and ii), regions annotated asproximal to genes (using the definitions from set v-vii), carry ahistone mark in Th17 cells (using the definition from set viii), orbound by transcription regulators in Th17 cells (using the definitionsfrom set ix).

For the transcription regulators (set ix), an additional “gene-level”test was also included: here the overlap between the set of bound genesusing a hypergeometric p-value was evaluated. A similar test was used toevaluate the overlap between the bound genes and genes that aredifferentially expressed in Tsc22d3 knockdown.

The analysis was repeated with a second peak-calling software(Scripture) (Guttman, M. et al. in Nature biotechnology Vol. 28 503-510(2010); Garber, M. et al. A High-Throughput ChromatinImmunoprecipitation Approach Reveals Principles of Dynamic GeneRegulation in Mammals. Molecular cell, doi:10.1016/j.molcel.2012.07.030(2012)), and obtained consistent results in all the above tests.Specifically, similar levels of overlap with the Th17 factors tested,both in terms of co-occupied binding sites and in terms of common targetgenes, was seen.

Estimating Statistical Significance of Monochromatic InteractionsBetween Modules:

The functional network in FIG. 4b consists of two modules: positive andnegative. Two indices were computed: (1) within-module index: thepercentage of positive edges between members of the same module (i.e.,down-regulation in knockdown/knockout); and, (2) between-module index:the percentage of negative edges between members of the same module thatare negative. The network was shuffled 1,000 times, while maintainingthe nodes' out degrees (i.e., number of outgoing edges) and edges' signs(positive/negative), and re-computed the two indices. The reportedp-values were computed using a t-test.

Using Literature Microarray Data for Deriving a Th17 Signature and forIdentifying Genes Responsive to Th17-Related Perturbations:

To define the Th17 signatures genes, the gene expression data from Weiet al., in Immunity, vol. 30 155-167 (2009) was downloaded and analyzed,and the data was preprocessed using the RMA algorithm, followed byquantile normalization using the default parameters in theExpressionFileCreator module of the 23 GenePattern suite (Reich, M. etal. GenePattern 2.0. Nat. Genet. 38, 500-501, doi:10.1038/ng0506-500(2006)). This data includes replicate microarray measurements from Th17,Th1, Th2, iTreg, nTreg, and Naïve CD4+ T cells. For each gene, it wasevaluated whether it is over-expressed in Th17 cells compared to allother cell subsets using a one-sided t-test. All cases that had ap-value<0.05 were retained. As an additional filtering step, it wasrequired that the expression level of a gene in Th17 cells be at least1.25 fold higher than its expression in all other cell subsets. To avoidspurious fold levels due to low expression values, a small constant(c=50) was added to the expression values.

To define genes responsive to published Th17-related perturbations, geneexpression data from several sources that provided transcriptionalprofiles of Th17 cells under various conditions (listed above) weredownloaded and analyzed. These datasets were preprocessed as above. Tofind genes that were differentially expressed in a given condition(compared to their respective control), the fold change between theexpression levels of each probeset in the case and control conditionswas computed. To avoid spurious fold levels due to low expressionvalues, a small constant as above was added to the expression values.Only cases where more than 50% of all of the possible case-controlcomparisons were above a cutoff of 1.5 fold change were reported. As anadditional filter, when duplicates are available, a Z-score was computedas above and only cases with a corresponding p-value<0.05 were reported.

Genes:

The abbreviations set forth below in Table 11 are used herein toidentify the genes used throughout the disclosure, including but notlimited to those shown in Tables 1-9 of the specification.

TABLE 11 Gene Abbreviations, Entrez ID Numbers and Brief DescriptionSymbol Entrez ID Description AAK1 22848 AP2 associated kinase 1 ABCG29429 ATP-binding cassette, sub-family G (WHITE), member 2 ACP5 54 acidphosphatase 5, tartrate resistant ACVR1B 91 activin A receptor, type 1BACVR2A 92 activin receptor IIA ADAM10 102 a disintegrin andmetallopeptidase domain 10 ADAM17 6868 a disintegrin andmetallopeptidase domain 17 ADRBK1 156 adrenergic receptor kinase, beta 1AES 166 amino-terminal enhancer of split AHR 196 aryl-hydrocarbonreceptor AIM1 202 absent in melanoma 1 AKT1 207 thymoma viralproto-oncogene 1 ALPK2 115701 alpha-kinase 2 ANKHD1 54882 ankyrin repeatand KH domain containing 1 ANP32A 8125 acidic (leucine-rich) nuclearphosphoprotein 32 family, member A ANXA4 307 annexin A4 AQP3 360aquaporin 3 ARHGEF3 50650 Rho guanine nucleotide exchange factor (GEF) 3ARID3A 1820 AT rich interactive domain 3A (BRIGHT-like) ARID5A 10865 ATrich interactive domain 5A (MRF1-like) ARL5A 26225 ADP-ribosylationfactor-like 5A ARMCX2 9823 armadillo repeat containing, X-linked 2 ARNTL406 aryl hydrocarbon receptor nuclear translocator-like ASXL1 171023additional sex combs like 1 (Drosophila) ATF2 1386 activatingtranscription factor 2 ATF3 467 activating transcription factor 3 ATF4468 activating transcription factor 4 AURKB 9212 aurora kinase B AXL 558AXL receptor tyrosine kinase B4GALT1 2683 UDP-Gal: betaGlcNAc beta1,4-galactosyltransferase, polypeptide 1 BATF 10538 basic leucine zippertranscription factor, ATF-like BATF3 55509 basic leucine zippertranscription factor, ATF-like 3 BAZ2B 29994 bromodomain adjacent tozinc finger domain, 2B BCL11B 64919 B-cell leukemia/lymphoma 11B BCL2L1110018 BCL2-like 11 (apoptosis facilitator) BCL3 602 B-cellleukemia/lymphoma 3 BCL6 604 B-cell leukemia/lymphoma 6 BHLH40 8553Basic Helix-Loop-Helix Family, Member E40 BLOC1S1 2647 biogenesis oflysosome-related organelles complex-1, subunit 1 BMP2K 55589 BMP2inducible kinase BMPR1A 657 bone morphogenetic protein receptor, type 1ABPGM 669 2,3-bisphosphoglycerate mutase BSG 682 basigin BTG1 694 B-celltranslocation gene 1, anti-proliferative BTG2 7832 B-cell translocationgene 2, anti-proliferative BUB1 699 budding uninhibited bybenzimidazoles 1 homolog (S. cerevisiae) C14ORF83 161145 RIKEN cDNA6330442E10 gene C16ORF80 29105 gene trap locus 3 C21ORF66 94104 RIKENcDNA 1810007M14 gene CAMK4 814 calcium/calmodulin-dependent proteinkinase IV CARM1 10498 coactivator-associated arginine methyltransferase1 CASP1 834 caspase 1 CASP3 836 caspase 3 CASP4 837 caspase 4,apoptosis-related cysteine peptidase CASP6 839 caspase 6 CASP8AP2 9994caspase 8 associated protein 2 CBFB 865 core binding factor beta CBX48535 chromobox homolog 4 (Drosophila Pc class) CCL1 6346 chemokine (C-Cmotif) ligand 1 CCL20 6364 chemokine (C-C motif) ligand 20 CCL4 6351chemokine (C-C motif) ligand 4 CCND2 894 cyclin D2 CCR4 1233 chemokine(C-C motif) receptor 4 CCR5 1234 chemokine (C-C motif) receptor 5 CCR61235 chemokine (C-C motif) receptor 6 CCR8 1237 chemokine (C-C motif)receptor 8 CCRN4L 25819 CCR4 carbon catabolite repression 4-like (S.cerevisiae) CD14 929 CD14 antigen CD2 914 CD2 antigen CD200 4345 CD200antigen CD226 10666 CD226 antigen CD24 934 CD24a antigen CD247 919 CD247antigen CD27 939 CD27 antigen CD274 29126 CD274 antigen CD28 940 CD28antigen CD3D 915 CD3 antigen, delta polypeptide CD3G 917 CD3 antigen,gamma polypeptide CD4 920 CD4 antigen CD40LG 959 CD40 ligand CD44 960CD44 antigen CD53 963 CD53 antigen CD5L 922 CD5 antigen-like CD63 967CD63 antigen CD68 968 CD68 antigen CD70 970 CD70 antigen CD74 972 CD74antigen (invariant polypeptide of major histocompatibility complex, clCD80 941 CD80 antigen CD83 9308 CD83 antigen CD84 8832 CD84 antigen CD86942 CD86 antigen CD9 928 CD9 antigen CD96 10225 CD96 antigen CDC25B 994cell division cycle 25 homolog B (S. pombe) CDC42BPA 8476 CDC42 bindingprotein kinase alpha CDC5L 988 cell division cycle 5-like (S. pombe)CDK5 1020 cyclin-dependent kinase 5 CDK6 1021 cyclin-dependent kinase 6CDKN3 1033 cyclin-dependent kinase inhibitor 3 CDYL 9425 chromodomainprotein, Y chromosome-like CEBPB 1051 CCAAT/enhancer binding protein(C/EBP), beta CENPT 80152 centromere protein T CHD7 55636 chromodomainhelicase DNA binding protein 7 CHMP1B 57132 chromatin modifying protein1B CHMP2A 27243 charged multivesicular body protein 2A CHRAC1 54108chromatin accessibility complex 1 CIC 23152 capicua homolog (Drosophila)CITED2 10370 Cbp/p300-interacting transactivator, with Glu/Asp-richcarboxy-terminal dom CLCF1 23529 cardiotrophin-like cytokine factor 1CLK1 1195 CDC-like kinase 1 CLK3 1198 CDC-like kinase 3 CMTM6 54918CKLF-like MARVEL transmembrane domain containing 6 CNOT2 4848 CCR4-NOTtranscription complex, subunit 2 CREB1 1385 cAMP responsive elementbinding protein 1 CREB3L2 64764 cAMP responsive element binding protein3-like 2 CREG1 8804 cellular repressor of E1A-stimulated genes 1 CREM1390 cAMP responsive element modulator CSDA 8531 cold shock domainprotein A CSF1R 1436 colony stimulating factor 1 receptor CSF2 1437colony stimulating factor 2 (granulocyte-macrophage) CTLA4 1493cytotoxic T-lymphocyte-associated protein 4 CTSD 1509 cathepsin D CTSW1521 cathepsin W CXCL10 3627 chemokine (C-X-C motif) ligand 10 CXCR32833 chemokine (C-X-C motif) receptor 3 CXCR4 7852 chemokine (C-X-Cmotif) receptor 4 CXCR5 643 chemochine (C-X-C motif) receptor 5 DAPP127071 dual adaptor for phosphotyrosine and 3- phosphoinositides 1 DAXX1616 Fas death domain-associated protein DCK 1633 deoxycytidine kinaseDCLK1 9201 doublecortin-like kinase 1 DDIT3 1649 DNA-damage inducibletranscript 3 DDR1 780 discoidin domain receptor family, member 1 DGKA1606 diacylglycerol kinase, alpha DGUOK 1716 deoxyguanosine kinaseDNAJC2 27000 DnaJ (Hsp40) homolog, subfamily C, member 2 DNTT 1791deoxynucleotidyltransferase, terminal DPP4 1803 dipeptidylpeptidase 4DUSP1 1843 dual specificity phosphatase 1 DUSP10 11221 dual specificityphosphatase 10 DUSP14 11072 dual specificity phosphatase 14 DUSP16 80824dual specificity phosphatase 16 DUSP2 1844 dual specificity phosphatase2 DUSP22 56940 dual specificity phosphatase 22 DUSP6 1848 dualspecificity phosphatase 6 E2F1 1869 E2F transcription factor 1 E2F4 1874E2F transcription factor 4 E2F8 79733 E2F transcription factor 8 ECE29718 endothelin converting enzyme 2 EGR1 1958 early growth response 1EGR2 1959 early growth response 2 EIF2AK2 5610 eukaryotic translationinitiation factor 2-alpha kinase 2 ELK3 2004 ELK3, member of ETSoncogene family ELL2 22936 elongation factor RNA polymerase II 2 EMP12012 epithelial membrane protein 1 ENTPD1 953 ectonucleosidetriphosphate diphosphohydrolase 1 ERCC5 2073 excision repaircross-complementing rodent repair deficiency, complementati ERRFI1 54206ERBB receptor feedback inhibitor 1 ETS1 2113 E26 avian leukemia oncogene1, 5′ domain ETS2 2114 E26 avian leukemia oncogene 2, 3′ domain ETV62120 ets variant gene 6 (TEL oncogene) EZH1 2145 enhancer of zestehomolog 1 (Drosophila) FAS 355 Fas (TNF receptor superfamily member 6)FASLG 356 Fas ligand (TNF superfamily, member 6) FCER1G 2207 Fcreceptor, IgE, high affinity I, gamma polypeptide FCGR2B 2213 Fcreceptor, IgG, low affinity IIb FES 2242 feline sarcoma oncogene FLI12313 Friend leukemia integration 1 FLNA 2316 filamin, alpha FOSL2 2355fos-like antigen 2 FOXJ2 55810 forkhead box J2 FOXM1 2305 forkhead boxM1 FOXN3 1112 forkhead box N3 FOXO1 2308 forkhead box O1 FOXP1 27086forkhead box P1 FOXP3 50943 forkhead box P3 FRMD4B 23150 FERM domaincontaining 4B FUS 2521 fusion, derived from t(12; 16) malignantliposarcoma (human) FZD7 8324 frizzled homolog 7 (Drosophila) GAP43 2596growth associated protein 43 GATA3 2625 GATA binding protein 3 GATAD157798 GATA zinc finger domain containing 1 GATAD2B 57459 GATA zincfinger domain containing 2B GEM 2669 GTP binding protein (geneoverexpressed in skeletal muscle) GFI1 2672 growth factor independent 1GJA1 2697 gap junction protein, alpha 1 GK 2710 glycerol kinase GLIPR111010 GLI pathogenesis-related 1 (glioma) GMFB 2764 glia maturationfactor, beta GMFG 9535 glia maturation factor, gamma GRN 2896 granulinGUSB 2990 glucuronidase, beta HCLS1 3059 hematopoietic cell specific Lynsubstrate 1 HDAC8 55869 histone deacetylase 8 HIF1A 3091 hypoxiainducible factor 1, alpha subunit HINT3 135114 histidine triadnucleotide binding protein 3 HIP1R 9026 huntingtin interacting protein 1related HIPK1 204851 homeodomain interacting protein kinase 1 HIPK228996 homeodomain interacting protein kinase 2 HK1 3098 hexokinase 1 HK23099 hexokinase 2 HLA-A 3105 major histocompatibility complex, class I,A HLA-DQA1 3117 histocompatibility 2, class II antigen A, alpha HMGA13159 high mobility group AT-hook 1 HMGB2 3148 high mobility group box 2HMGN1 3150 high mobility group nucleosomal binding domain 1 ICOS 29851inducible T-cell co-stimulator ID1 3397 inhibitor of DNA binding 1 ID23398 inhibitor of DNA binding 2 ID3 3399 inhibitor of DNA binding 3 IER38870 immediate early response 3 IFI35 3430 interferon-induced protein 35IFIH1 64135 interferon induced with helicase C domain 1 IFIT1 3434interferon-induced protein with tetratricopeptide repeats 1 IFITM2 10581interferon induced transmembrane protein 2 IFNG 3458 interferon gammaIFNGR1 3459 interferon gamma receptor 1 IFNGR2 3460 interferon gammareceptor 2 IKZF1 10320 IKAROS family zinc finger 1 IKZF3 22806 IKAROSfamily zinc finger 3 IKZF4 64375 IKAROS family zinc finger 4 IL10 3586interleukin 10 IL10RA 3587 interleukin 10 receptor, alpha IL12RB1 3594interleukin 12 receptor, beta 1 IL12RB2 3595 interleukin 12 receptor,beta 2 IL15RA 3601 interleukin 15 receptor, alpha chain IL17A 3605interleukin 17A IL17F 112744 interleukin 17F IL17RA 23765 interleukin 17receptor A IL18R1 8809 interleukin 18 receptor 1 IL1R1 3554 interleukin1 receptor, type I IL1RN 3557 interleukin 1 receptor antagonist IL2 3558interleukin 2 IL21 59067 interleukin 21 IL21R 50615 interleukin 21receptor IL22 50616 interleukin 22 IL23R 149233 interleukin 23 receptorIL24 11009 interleukin 24 IL27RA 9466 interleukin 27 receptor, alphaIL2RA 3559 interleukin 2 receptor, alpha chain IL2RB 3560 interleukin 2receptor, beta chain IL2RG 3561 interleukin 2 receptor, gamma chain IL33562 interleukin 3 IL4 3565 interleukin 4 IL4R 3566 interleukin 4receptor, alpha IL6ST 3572 interleukin 6 signal transducer IL7R 3575interleukin 7 receptor IL9 3578 interleukin 9 INHBA 3624 inhibin beta-AINPP1 3628 inositol polyphosphate-1-phosphatase IRAK1BP1 134728interleukin-1 receptor-associated kinase 1 binding protein 1 IRF1 3659interferon regulatory factor 1 IRF2 3660 interferon regulatory factor 2IRF3 3661 interferon regulatory factor 3 IRF4 3662 interferon regulatoryfactor 4 IRF7 3665 interferon regulatory factor 7 IRF8 3394 interferonregulatory factor 8 IRF9 10379 interferon regulatory factor 9 ISG20 3669interferon-stimulated protein ITGA3 3675 integrin alpha 3 ITGAL 3683integrin alpha L ITGAV 3685 integrin alpha V ITGB1 3688 integrin beta 1(fibronectin receptor beta) ITK 3702 IL2-inducible T-cell kinase JAK23717 Janus kinase 2 JAK3 3718 Janus kinase 3 JARID2 3720 jumonji, ATrich interactive domain 2 JMJD1C 221037 jumonji domain containing 1C JUN3725 Jun oncogene JUNB 3726 Jun-B oncogene KAT2B 8850 K(lysine)acetyltransferase 2B KATNA1 11104 katanin p60 (ATPase-containing)subunit A1 KDM6B 23135 lysine (K)-specific demethylase 6B KLF10 7071Kruppel-like factor 10 KLF13 51621 Kruppel-like factor 13 KLF6 1316Kruppel-like factor 6 KLF7 8609 Kruppel-like factor 7 (ubiquitous) KLF9687 Kruppel-like factor 9 KLRD1 3824 killer cell lectin-like receptor,subfamily D, member 1 LAD1 3898 ladinin LAMP2 3920 lysosomal-associatedmembrane protein 2 LASS4 79603 LAG1 homolog, ceramide synthase 4 LASS6253782 LAG1 homolog, ceramide synthase 6 LEF1 51176 lymphoid enhancerbinding factor 1 LGALS3BP 3959 lectin, galactoside-binding, soluble, 3binding protein LGTN 1939 ligatin LIF 3976 leukemia inhibitory factorLILRB1, LILRB2, 10859, 10288, 11025, leukocyte immunoglobulin-likereceptor, subfamily B LILRB3, LILRB4, 11006, 10990 (with TM and ITIMdomains), members 1--5 LILRB5 LIMK2 3985 LIM motif-containing proteinkinase 2 LITAF 9516 LPS-induced TN factor LMNB1 4001 lamin B1 LRRFIP19208 leucine rich repeat (in FLII) interacting protein 1 LSP1 4046lymphocyte specific 1 LTA 4049 lymphotoxin A MAF 4094 avianmusculoaponeurotic fibrosarcoma (v-maf) AS42 oncogene homolog MAFF 23764v-maf musculoaponeurotic fibrosarcoma oncogene family, protein F (avian)MAFG 4097 v-maf musculoaponeurotic fibrosarcoma oncogene family, proteinG (avian) MAML2 84441 mastermind like 2 (Drosophila) MAP3K5 4217mitogen-activated protein kinase kinase kinase 5 MAP3K8 1326mitogen-activated protein kinase kinase kinase 8 MAP4K2 5871mitogen-activated protein kinase kinase kinase kinase 2 MAP4K3 8491mitogen-activated protein kinase kinase kinase kinase 3 MAPKAPK2 9261MAP kinase-activated protein kinase 2 MATR3 9782 matrin 3 MAX 4149 Maxprotein MAZ 4150 MYC-associated zinc finger protein (purine-bindingtranscription factor) MBNL1 4154 muscleblind-like 1 (Drosophila) MBNL355796 muscleblind-like 3 (Drosophila) MDM4 4194 transformed mouse 3T3cell double minute 4 MEN1 4221 multiple endocrine neoplasia 1 MFHAS19258 malignant fibrous histiocytoma amplified sequence 1 MGLL 11343monoglyceride lipase MIER1 57708 mesoderm induction early response 1homolog (Xenopus laevis MINA 84864 myc induced nuclear antigen MKNK22872 MAP kinase-interacting serine/threonine kinase 2 MORF4L1 10933mortality factor 4 like 1 MORF4L2 9643 mortality factor 4 like 2 MS4A6A64231 membrane-spanning 4-domains, subfamily A, member 6B MST4 51765serine/threonine protein kinase MST4 MT1A 4489 metallothionein 1 MT2A4502 metallothionein 2 MTA3 57504 metastasis associated 3 MXD3 83463 Maxdimerization protein 3 MXI1 4601 Max interacting protein 1 MYC 4609myelocytomatosis oncogene MYD88 4615 myeloid differentiation primaryresponse gene 88 MYST4 23522 MYST histone acetyltransferase monocyticleukemia 4 NAGK 55577 N-acetylglucosamine kinase NAMPT 10135nicotinamide phosphoribosyltransferase NASP 4678 nuclear autoantigenicsperm protein (histone-binding) NCF1C 654817 neutrophil cytosolic factor1 NCOA1 8648 nuclear receptor coactivator 1 NCOA3 8202 nuclear receptorcoactivator 3 NEK4 6787 NIMA (never in mitosis gene a)-related expressedkinase 4 NEK6 10783 NIMA (never in mitosis gene a)-related expressedkinase 6 NFATC1 4772 nuclear factor of activated T-cells, cytoplasmic,calcineurin-dependent 1 NFATC2 4773 nuclear factor of activated T-cells,cytoplasmic, calcineurin-dependent 2 NFE2L2 4780 nuclear factor,erythroid derived 2, like 2 NFIL3 4783 nuclear factor, interleukin 3,regulated NFKB1 4790 nuclear factor of kappa light polypeptide geneenhancer in B-cells 1, p105 NFKBIA 4792 nuclear factor of kappa lightpolypeptide gene enhancer in B-cells inhibito NFKBIB 4793 nuclear factorof kappa light polypeptide gene enhancer in B-cells inhibito NFKBIE 4794nuclear factor of kappa light polypeptide gene enhancer in B-cellsinhibito NFKBIZ 64332 nuclear factor of kappa light polypeptide geneenhancer in B-cells inhibito NFYC 4802 nuclear transcription factor-Ygamma NKG7 4818 natural killer cell group 7 sequence NMI 9111 N-myc (andSTAT) interactor NOC4L 79050 nucleolar complex associated 4 homolog (S.cerevisiae) NOTCH1 4851 Notch gene homolog 1 (Drosophila) NOTCH2 4853Notch gene homolog 2 (Drosophila) NR3C1 2908 nuclear receptor subfamily3, group C, member 1 NR4A2 4929 nuclear receptor subfamily 4, group A,member 2 NR4A3 8013 nuclear receptor subfamily 4, group A, member 3NUDT4 11163 nudix (nucleoside diphosphate linked moiety X)-type motif 4OAS2 4939 2′-5′ oligoadenylate synthetase 2 PACSIN1 29993 protein kinaseC and casein kinase substrate in neurons 1 PAXBP1 94104 PAX3 and PAX7binding protein 1 PCTK1 5127 PCTAIRE-motif protein kinase 1 PDCD1 5133programmed cell death 1 PDCD1LG2 80380 programmed cell death 1 ligand 2PDK3 5165 pyruvate dehydrogenase kinase, isoenzyme 3 PDPK1 51703-phosphoinositide dependent protein kinase-1 PDXK 8566 pyridoxal(pyridoxine, vitamin B6) kinase PECI 10455 peroxisomal delta3,delta2-enoyl-Coenzyme A isomerase PELI2 57161 pellino 2 PGK1 5230phosphoglycerate kinase 1 PHACTR2 9749 phosphatase and actin regulator 2PHF13 148479 PHD finger protein 13 PHF21A 51317 PHD finger protein 21APHF6 84295 PHD finger protein 6 PHLDA1 22822 pleckstrin homology-likedomain, family A, member 1 PHLPP1 23239 PH domain and leucine richrepeat protein phosphatase 1 PI4KA 5297 phosphatidylinositol 4-kinase,catalytic, alpha polypeptide PIM1 5292 proviral integration site 1 PIM211040 proviral integration site 2 PIP4K2A 5305phosphatidylinositol-5-phosphate 4-kinase, type II, alpha PKM2 5315pyruvate kinase, muscle PLAC8 51316 placenta-specific 8 PLAGL1 5325pleiomorphic adenoma gene-like 1 PLAUR 5329 plasminogen activator,urokinase receptor PLEK 5341 pleckstrin PLEKHF2 79666 pleckstrinhomology domain containing, family F (with FYVE domain) member 2 PLK210769 polo-like kinase 2 (Drosophila) PMEPA1 56937 prostatetransmembrane protein, androgen induced 1 PML 5371 promyelocyticleukemia PNKP 11284 polynucleotide kinase 3′-phosphatase POU2AF1 5450POU domain, class 2, associating factor 1 POU2F2 5452 POU domain, class2, transcription factor 2 PPME1 51400 protein phosphatase methylesterase1 PPP2R5A 5525 protein phosphatase 2, regulatory subunit B (B56), alphaisoform PPP3CA 5530 protein phosphatase 3, catalytic subunit, alphaisoform PRC1 9055 protein regulator of cytokinesis 1 PRDM1 639 PR domaincontaining 1, with ZNF domain PRF1 5551 perforin 1 (pore formingprotein) PRICKLE1 144165 prickle like 1 (Drosophila) PRKCA 5578 proteinkinase C, alpha PRKCD 5580 protein kinase C, delta PRKCH 5583 proteinkinase C, eta PRKCQ 5588 protein kinase C, theta PRKD3 23683 proteinkinase D3 PRNP 5621 prion protein PROCR 10544 protein C receptor,endothelial PRPF4B 8899 PRP4 pre-mRNA processing factor 4 homolog B(yeast) PRPS1 5631 phosphoribosyl pyrophosphate synthetase 1 PSMB9 5698proteasome (prosome, macropain) subunit, beta type 9 (largemultifunctional PSTPIP1 9051 proline-serine-threoninephosphatase-interactingprotein 1 PTEN 5728 phosphatase and tensinhomolog PTK2B 2185 PTK2 protein tyrosine kinase 2 beta PTP4A1 7803protein tyrosine phosphatase 4a1 PTPLA 9200 protein tyrosinephosphatase-like (proline instead of catalytic arginine), PTPN1 5770protein tyrosine phosphatase, non-receptor type 1 PTPN18 26469 proteintyrosine phosphatase, non-receptor type 18 PTPN6 5777 protein tyrosinephosphatase, non-receptor type 6 PTPRC 5788 protein tyrosinephosphatase, receptor type, C PTPRCAP 5790 protein tyrosine phosphatase,receptor type, C polypeptide-associated prote PTPRE 5791 proteintyrosine phosphatase, receptor type, E PTPRF 5792 protein tyrosinephosphatase, receptor type, F PTPRJ 5795 protein tyrosine phosphatase,receptor type, J PTPRS 5802 protein tyrosine phosphatase, receptor type,S PVR 5817 poliovirus receptor PYCR1 5831 pyrroline-5-carboxylatereductase 1 RAB33A 9363 RAB33A, member of RAS oncogene family RAD51AP110635 RAD51 associated protein 1 RARA 5914 retinoic acid receptor, alphaRASGRP1 10125 RAS guanyl releasing protein 1 RBPJ 3516 recombinationsignal binding protein for immunoglobulin kappa J region REL 5966reticuloendotheliosis oncogene RELA 5970 v-rel reticuloendotheliosisviral oncogene homolog A (avian) RFK 55312 riboflavin kinase RIPK1 8737receptor (TNFRSF)-interacting serine-threonine kinase 1 RIPK2 8767receptor (TNFRSF)-interacting serine-threonine kinase 2 RIPK3 11035receptor-interacting serine-threonine kinase 3 RNASEL 6041 ribonucleaseL (2′,5′-oligoisoadenylate synthetase- dependent) RNF11 26994 ringfinger protein 11 RNF5 6048 ring finger protein 5 RORA 6095 RAR-relatedorphan receptor alpha RORC 6097 RAR-related orphan receptor gamma RPP1411102 ribonuclease P 14 subunit (human) RPS6KB1 6198 ribosomal proteinS6 kinase, polypeptide 1 RUNX1 861 runt related transcription factor 1RUNX2 860 runt related transcription factor 2 RUNX3 864 runt relatedtranscription factor 3 RXRA 6256 retinoid X receptor alpha SAP18 10284Sin3-associated polypeptide 18 SAP30 8819 sin3 associated polypeptideSATB1 6304 special AT-rich sequence binding protein 1 SEMA4D 10507 semadomain, immunoglobulin domain (Ig), transmembrane domain (TM) and shorSEMA7A 8482 sema domain, immunoglobulin domain (Ig), and GPI membraneanchor, (semaphor SERPINB1 1992 serine (or cysteine) peptidaseinhibitor, clade B, member 1a SERPINE2 5270 serine (or cysteine)peptidase inhibitor, clade E, member 2 SERTAD1 29950 SERTA domaincontaining 1 SGK1 6446 serum/glucocorticoid regulated kinase 1 SH2D1A4068 SH2 domain protein 1A SIK1 150094 salt-inducible kinase 1 SIRT222933 sirtuin 2 (silent mating type information regulation 2, homolog) 2(S. cere SKAP2 8935 src family associated phosphoprotein 2 SKI 6497 skisarcoma viral oncogene homolog (avian) SKIL 6498 SKI-like SLAMF7 57823SLAM family member 7 SLC2A1 6513 solute carrier family 2 (facilitatedglucose transporter), member 1 SLC3A2 6520 solute carrier family 3(activators of dibasic and neutral amino acid trans SLK 9748 STE20-likekinase (yeast) SMAD2 4087 MAD homolog 2 (Drosophila) SMAD3 4088 MADhomolog 3 (Drosophila) SMAD4 4089 MAD homolog 4 (Drosophila) SMAD7 4092MAD homolog 7 (Drosophila) SMARCA4 6597 SWI/SNF related, matrixassociated, actin dependent regulator of chromatin, SMOX 54498 spermineoxidase SOCS3 9021 suppressor of cytokine signaling 3 SP1 6667trans-acting transcription factor 1 SP100 6672 nuclear antigen Sp100 SP46671 trans-acting transcription factor 4 SPHK1 8877 sphingosine kinase 1SPOP 8405 speckle-type POZ protein SPP1 6696 secreted phosphoprotein 1SPRY1 10252 sprouty homolog 1 (Drosophila) SRPK2 6733serine/arginine-rich protein specific kinase 2 SS18 6760 synovialsarcoma translocation, Chromosome 18 STARD10 10809 START domaincontaining 10 STAT1 6772 signal transducer and activator oftranscription 1 STAT2 6773 signal transducer and activator oftranscription 2 STAT3 6774 signal transducer and activator oftranscription 3 STAT4 6775 signal transducer and activator oftranscription 4 STAT5A 6776 signal transducer and activator oftranscription 5A STAT5B 6777 signal transducer and activator oftranscription 5B STAT6 6778 signal transducer and activator oftranscription 6 STK17B 9262 serine/threonine kinase 17b(apoptosis-inducing) STK19 8859 serine/threonine kinase 19 STK38 11329serine/threonine kinase 38 STK38L 23012 serine/threonine kinase 38 likeSTK39 27347 serine/threonine kinase 39, STE20/SPS1 homolog (yeast) STK46789 serine/threonine kinase 4 SULT2B1 6820 sulfotransferase family,cytosolic, 2B, member 1 SUZ12 23512 suppressor of zeste 12 homolog(Drosophila) TAF1B 9014 TATA box binding protein (Tbp)-associatedfactor, RNA polymerase I, B TAL2 6887 T-cell acute lymphocytic leukemia2 TAP1 6890 transporter 1, ATP-binding cassette, sub-family B (MDR/TAP)TBPL1 9519 TATA box binding protein-like 1 TBX21 30009 T-box 21 TCERG110915 transcription elongation regulator 1 (CA150) TEC 7006 cytoplasmictyrosine kinase, Dscr28C related (Drosophila) TFDP1 7027 transcriptionfactor Dp 1 TFEB 7942 transcription factor EB TGFB1 7040 transforminggrowth factor, beta 1 TGFB3 7043 transforming growth factor, beta 3TGFBR1 7046 transforming growth factor, beta receptor I TGFBR3 7049transforming growth factor, beta receptor III TGIF1 7050 TGFB-inducedfactor homeobox 1 TGM2 7052 transglutaminase 2, C polypeptide THRAP39967 thyroid hormone receptor associated protein 3 TIMP2 7077 tissueinhibitor of metalloproteinase 2 TK1 7083 thymidine kinase 1 TK2 7084thymidine kinase 2, mitochondrial TLE1 7088 transducin-like enhancer ofsplit 1, homolog of Drosophila E(spl) TLR1 7096 toll-like receptor 1TMEM126A 84233 transmembrane protein 126A TNFRSF12A 51330 tumor necrosisfactor receptor superfamily, member 12a TNFRSF13B 23495 tumor necrosisfactor receptor superfamily, member 13b TNFRSF1B 7133 tumor necrosisfactor receptor superfamily, member 1b TNFRSF25 8718 tumor necrosisfactor receptor superfamily, member 25 TNFRSF4 7293 tumor necrosisfactor receptor superfamily, member 4 TNFRSF9 3604 tumor necrosis factorreceptor superfamily, member 9 TNFSF11 8600 tumor necrosis factor(ligand) superfamily, member 11 TNFSF8 944 tumor necrosis factor(ligand) superfamily, member 8 TNFSF9 8744 tumor necrosis factor(ligand) superfamily, member 9 TNK2 10188 tyrosine kinase, non-receptor,2 TOX4 9878 TOX high mobility group box family member 4 TP53 7157transformation related protein 53 TRAF3 7187 Tnf receptor-associatedfactor 3 TRAT1 50852 T cell receptor associated transmembrane adaptor 1TRIM24 8805 tripartite motif-containing 24 TRIM25 7706 tripartitemotif-containing 25 TRIM28 10155 tripartite motif-containing 28 TRIM585363 tripartite motif containing 5 TRIP12 9320 thyroid hormone receptorinteractor 12 TRPS1 7227 trichorhinophalangeal syndrome I (human) TRRAP8295 transformation/transcription domain-associated protein TSC22D3 1831TSC22 domain family, member 3 TSC22D4 81628 TSC22 domain family, member4 TWF1 5756 twinfilin, actin-binding protein, homolog 1 (Drosophila) TXK7294 TXK tyrosine kinase UBE2B 7320 ubiquitin-conjugating enzyme E2B,RAD6 homology (S. cerevisiae) UBIAD1 29914 UbiA prenyltransferase domaincontaining 1 ULK2 9706 Unc-51 like kinase 2 (C. elegans) VAV1 7409 vav 1oncogene VAV3 10451 vav 3 oncogene VAX2 25806 ventral anterior homeoboxcontaining gene 2 VRK1 7443 vaccinia related kinase 1 VRK2 7444 vacciniarelated kinase 2 WDHD1 11169 WD repeat and HMG-box DNA binding protein 1WHSC1L1 54904 Wolf-Hirschhorn syndrome candidate 1-like 1 (human) WNK165125 WNK lysine deficient protein kinase 1 XAB2 56949 XPA bindingprotein 2 XBP1 7494 X-box binding protein 1 XRCC5 7520 X-ray repaircomplementing defective repair in Chinese hamster cells 5 YBX1 4904 Ybox protein 1 ZAK 51776 RIKEN cDNA B230120H23 gene ZAP70 7535 zeta-chain(TCR) associated protein kinase ZBTB32 27033 zinc finger and BTB domaincontaining 32 ZEB1 6935 zinc finger E-box binding homeobox 1 ZEB2 9839zinc finger E-box binding homeobox 2 ZFP161 7541 zinc finger protein 161ZFP36L1 677 zinc finger protein 36, C3H type-like 1 ZFP36L2 678 zincfinger protein 36, C3H type-like 2 ZFP62 92379 zinc finger protein 62ZNF238 10472 zinc finger protein 238 ZNF281 23528 zinc finger protein281 ZNF326 284695 zinc finger protein 326 ZNF703 80139 zinc fingerprotein 703 ZNRF1 84937 zinc and ring finger 1 ZNRF2 223082 zinc andring finger 2

Primers for Nanostring STA and qRT-PCR/Fluidigm and siRNA Sequences:

Table S6.1 presents the sequences for each forward and reverse primerused in the Fluidigm/qRT-PCR experiments and Nanostring nCounter geneexpression profiling. Table S6.2 presents the sequences for RNAi usedfor knockdown analysis.

TABLE S6.1 Primer Sequences SEQ SEQ Gene ID ID Assay Name NO: ForwardSequence NO: Reverse Sequence Nanostring 1700097N02Rik 1 GGC CAG AGC TTG2 AGC AAG CCA GCC STA ACC ATC AAA CAG Nanostring Aim1 3 AGC CAA TTT TGA4 GGA AGC CCT GCA STA AGG GCA TTT CCT Nanostring Arntl 5 TAT AAC CCC TGG6 GTT GCA GCC CTC STA GCC CTC GTT GTC Nanostring Bcl6 7 GTC GGG ACA TCT8 GGA GGA TGC AAA STA TGA CGG ACC CCT Nanostring Ccl20 9 GCA TGG GTA CTG10 TGA GGA GGT TCA STA CTG GCT CAG CCC Nanostring Cd24a 11 GGA CGC GTGAAA 12 TGC ACT ATG GCC STA GGT TTG TTA TCG G Nanostring Cd80 13 TGC CTAAGC TCC 14 ACG GCA AGG CAG STA ATT GGC CAA TAC Nanostring Csnk1a1 15 GGGTAT TGG GCG 16 CCA CGG CAG ACT STA TCA CTG GGT TCT Nanostring Ddr1 17ATG CAC ACT CTG 18 CCA AGG ACC TGC STA GGA GCC AAA GAG G Nanostring Emp119 AGC TGC CAT ACC 20 AGG CAC ATG GGA STA ACT GGC TCT GGA NanostringFlna 21 CTT CAC TGC ATT 22 CAC AGG ACA ACG STA CGC CCT GAA GCANanostring Gata3 23 CAC CGC CAT GGG 24 TGG GAT CCG GAT STA TTA GAG TCAGTG Nanostring 2900064A13Rik 25 AAG GAA AAA TGC 26 TCT CCC GTC TCA STAGAG CAA GA TGT CAG G Nanostring Anxa4 27 ATG GGG GAC AGA 28 TGC CTA AGCCCT STA CGA GGT TCA TGG Nanostring Atf4 29 GAT GAT GGC TTG 30 TGG CCAATT GGG STA GCC AGT TTC ACT Nanostring Bmpr1a 31 CAT TTG GGA AAT 32 ATGGGC CCA ACA STA GGC TCG TTC TGA Nanostring Ccl4 33 AAG CTC TGC GTG 34ACC ACA GCT GGC STA TCT GCC TTG GAG Nanostring Cd274 35 CGT GGA TCC AGC36 ATC ATT CGC TGT STA CAC TTC GGC GTT Nanostring Cd86 37 ATC TGC CGTGCC 38 ACG AGC CCA TGT STA CAT TTA CCT TGA Nanostring Ctla2b 39 GGC TCAACA GCA 40 TTA ATT TGA AGA STA GGA AGC CAT CAT GGC A Nanostring Dntt 41CCC AGA AGC CAC 42 TTC CAG CCC TTT STA AGA GGA CCT TCC Nanostring Ercc543 GTG CCA TTT GAC 44 CTG GCC TAC CCT STA ACA GCG CCA CCT NanostringFoxm1 45 CAA GCC AGG CTG 46 TGG GTC GTT TCT STA GAA GAA GCT GTGNanostring Gem 47 GAC ACG CTT CGG 48 CAA CTG TGA TGA STA GTT CAC GGC CAGC Nanostring 6330442E10Rik 49 CCC AGC ATT AAG 50 AGG AGC AAC AGG STA GCTCCA GGA CCT Nanostring Api5 51 CAG CTT TGA ACA 52 AGC TGA CTG AAA STACAG GGT CTT TTC CTC CCT Nanostring B4galt1 53 TCA CAG TGG ACA 54 CAC TCACCC TGG STA TCG GGA GCA TCT Nanostring Cand1 55 CTA CTG CAG GGA 56 GGGTCC CTC TTT STA GGA GCG AGG GCA Nanostring Ccr4 57 GTC CGT GCA GTT 58GGT TTG GGG ACA STA TGG CTT GGC TTT Nanostring Cd28 59 CCT TTG CAG TGA60 CGT TTT GAA AAT STA GTT GGG A CTG CAG AGA A Nanostring Cd9 61 GCG GGAAAC ACT 62 TGC TGA AGA TCA STA CAA AGC TGC CGA Nanostring Ctsw 63 GCCACT GGA GCT 64 TGA CCT CTC CTG STA GAA GGA CCC GTA Nanostring Dpp4 65CCC TGC TCC TGC 66 AAA TCT TCC GAC STA ATC TGT CCA GCC Nanostring Errfi167 TCC TGC TTT TCC 68 CCA GCA ACA CAA STA CAT CCA GAC CAG C NanostringFoxo1 69 TCC AGT CTG GGC 70 GGC AGC AGA GGG STA AAG AGG TGG ATANanostring Gfi1 71 ATG TCT TCC CTG 72 AAG CCC AAA GCA STA CCT CCC CAGACG Nanostring Abcg2 73 GGA ACA TCG GCC 74 CAT TCC AGC GGC STA TTC AAAATC ATA Nanostring Aqp3 75 CGG CAC AGC TGG 76 GGT TGA CGG CAT STA AATCTT AGC CAG Nanostring Batf 77 CTA CCC AGA GGC 78 AAC TAT CCA CCC STACCA GTG CCT GCC Nanostring Casp1 79 TCC TGA GGG CAA 80 GAT TTG GCT TGCSTA AGA GGA CTG GG Nanostring Ccr5 81 AAC TGA ATG GGG 82 TTA CAG CCG CCTSTA AGG TTG G TTC AGG Nanostring Cd4 83 CCA GCC CTG GAT 84 GCC ACT TTCATC STA CTC CTT ACC ACC A Nanostring Cebpb 85 TGC ACC GAG GGG 86 AAC CCCGCA GGA STA ACA C ACA TCT Nanostring Cxcl10 87 TGC CGT CAT TTT 88 CGTGGC AAT GAT STA CTG CCT CTC AAC A Nanostring Egr2 89 AGG ACC TTG ATG 90CTG GCA TCC AGG STA GAG CCC GTC AAC Nanostring Etv6 91 CAT GAG GGA GGA92 AAA TCC CTG CTA STA TGC TGG TCA AAA ATC C Nanostring Foxp1 93 GCT CTCTGT CTC 94 ACT CAC AAC CCA STA CAA GGG C GAC CGC Nanostring Gja1 95 GGCCTG ATG ACC 96 TCC CTA CTT TTG STA TGG AGA CCG CCT Nanostring Acly 97GAG GGC TGG GAC 98 GCA GCT GCC CAG STA CAT TG AAT CTT Nanostring Arhgef399 GCA GCA GGC TGT 100 TTC CTC CCC ACT STA TTC TTA CC CAT CCA NanostringBC021614 101 AAG GAG GGC AAG 102 GAG CTT GGG TCG STA GAC CAG GGA TTTNanostring Casp3 103 GGA GAT GGC TTG 104 ACT CGA ATT CCG STA CCA GAA TTGCCA Nanostring Ccr6 105 GCC AGA TCC ATG 106 TTT GGT TGC CTG STA ACT GACG GAC GAT Nanostring Cd44 107 CAG GGA ACA TCC 108 TAG CAT CAC CCT STAACC AGC TTG GGG Nanostring Chd7 109 CAT TGT CAG TGG 110 GAA TCA CAG GCTSTA GCG TCA CGC CC Nanostring Cxcr3 111 CCA GAT CTA CCG 112 CAT GAC CAGAAG STA CAG GGA GGG CAG Nanostring Eif3e 113 GTC AAC CAG GGA 114 CAG TTTTCC CCA STA TGG CAG GAG CGA Nanostring Fas 115 GCT GTG GAT CTG 116 CCCCCA TTC ATT STA GGC TGT TTG CAG Nanostring Foxp3 117 TGG AAA CAC CCA 118GGC AAG ACT CCT STA GCC ACT GGG GAT Nanostring Glipr1 119 TGG ATG GCTTCG 120 TGC AGC TGT GGG STA TCT GTG TTG TGT Nanostring Acvr1b 121 GTGCCG ACA TCT 122 GCA CTC CCG CAT STA ATG CCC CAT CTT Nanostring Arid5a123 GGC CTC GGG TCT 124 CTA GGC AGC TGG STA TTC AGT GCT CAC NanostringBcl11b 125 GGA GGG GTG GCT 126 AAG ATT CTC GGG STA TTC AA GTC CCANanostring Casp4 127 GGA ACA GCT GGG 128 GCC TGG GTC CAC STA CAA AGA ACTGAA Nanostring Ccr8 129 GTG GGT GTT TGG 130 ATC AAG GGG ATG STA GAC TGCGTG GCT Nanostring Cd5l 131 TGG GGG TAC CAC 132 GGG CGT GTA GCC STA GACTGT TTG AGA Nanostring Clcf1 133 AAT CCT CCT CGA 134 TGA CAC CTG CAA STACTG GGG TGC TGC Nanostring Cxcr4 135 CCG ATA GCC TGT 136 GTC GAT GCT GATSTA GGA TGG CCC CAC Nanostring Eif3h 137 AGC CTT CGC CAT 138 CGC CTT CAGCGA STA GTC AAC GAG AGA Nanostring Fasl 139 GCA AAT AGC CAA 140 GTT GCAAGA CTG STA CCC CAG ACC CCG Nanostring Frmd4b 141 GGA GTC CCA GTC 142TGG ACC TTC TTC STA CCA CCT TCC CCC Nanostring Golga3 143 TCC AAC CAGGTG 144 TCA TCT CAG AGT STA GAG CAC CCA GCC G Nanostring Acvr2a 145 ATGGCA AAC TTG 146 CAA GAT CTG TGC STA GAC CCC AGG GCA Nanostring Arl5a 147CGG ATT TGA GCG 148 AGT CAC TGG TGG STA CTT CTG GTG GGA NanostringBcl2l11 149 TGG CAA GCC CTC 150 AAA CAC ACA CAA STA TCA CTT CCA CGC ANanostring Casp6 151 TGC TCA AAA TTC 152 CAC GGG TAC GTC STA ACG AGG TGATG CTG Nanostring Cd2 153 CAC CCT GGT CGC 154 GGT TGT GTT GGG STA AGAGTT GCA TTC Nanostring Cd70 155 CTG GCT GTG GGC 156 GGA GTT GTG GTC STAATC TG AAG GGC Nanostring Cmtm6 157 TGC TGG TGT AGG 158 TCT CAG CAA TCASTA CGT CTT T CAG TGC AA Nanostring Cxcr5 159 TGG CCT TAA TGT 160 TGCTGG CTT GCC STA GCC TGT C CTT TAC Nanostring Eif3m 161 TGG CTT GTT ACA162 CCG ATG TGT GCT STA TGA GCA AAA GTG ACT G Nanostring Fip111 163 GGATAC GAA TGG 164 CCA ACG CTT GAA STA GAC TGG AA CTG GCT Nanostring Fzd7165 TTC CCT GCA ATA 166 TGA AGT AAT CTG STA GAA GTC TGG TCC TCC CGANanostring Grn 167 CCG GCC TAC TCA 168 AAC TTT ATT GGA STA TCC TGA GCAACA CAC G Nanostring Ahr 169 GTT GTG ATG CCA 170 CAA GCG TGC ATT STA AAGGGC GGA CTG Nanostring Armcx2 171 TCC AAT CTT GCC 172 TTC CAG CAC TTTSTA ACC ACC GGG AGC Nanostring Bcl3 173 CCA GGT TTT GCA 174 CCT CCC AGACCC STA CCA AGG CTC TGT Nanostring Ccl1 175 CAC TGA TGT GCC 176 TGA GGCGCA GCT STA TGC TGC TTC TCT Nanostring Cd247 177 TAC CAT CCC AGG 178 GCAGGT TGG CAG STA GAA GCA CAG TCT Nanostring Cd74 179 GCT TCC GAA ATC 180CGC CAT CCA TGG STA TGC CAA AGT TCT Nanostring Csf2 181 GGC CAT CAA AGA182 GCT GTC ATG TTC STA AGC CCT AAG GCG Nanostring Daxx 183 GTT GAC CCCGCA 184 ATT CCG AGG AGG STA CTG TCT CTT TGG Nanostring Elk3 185 CCT GTGGAC CCA 186 GAC GGA GTT CAG STA GAT GCT CTC CCA Nanostring Fli1 187 GATTCT GAG AAA 188 GCC AGT GTT CCA STA GGA GTA CGC A GTT GCC NanostringGap43 189 GCG AGA GAG CGA 190 CCA CGG AAG CTA STA GTG AGC GCC TGANanostring Gusb 191 ATG GAG CAG ACG 192 AAA GGC CGA AGT STA CAA TCC TTTGGG Nanostring H2-Q10 193 GTG GGC ATC TGT 194 TGG AGC GGG AGC STA GGTGGT ATA GTC Nanostring Ifi35 195 CAG AGT CCC ACT 196 AGG CAC AAC TGT STAGGA CCG CAG GGC Nanostring Il12rb2 197 GCA GCC AAC TCA 198 GTG ATG CTCCCT STA AAA GGC GGT TGG Nanostring Il22 199 TCA GAC AGG TTC 200 TCT TCTCGC TCA STA CAG CCC GAC GCA Nanostring Il4ra 201 CCT TCA GCC CCA 202 AGCTCA GCC TGG STA GTG GTA GTT CCT Nanostring Irf3 203 AAG GGA CAC TTC 204TTT CCT GCA GTT STA CCG GAG CCC CAG Nanostring Katna1 205 CGG TGC GGGAAC 206 CAT TTG GTC AAG STA TAT CC AAC TCC CTG Nanostring Lad1 207 GAAGGA GCT GTC 208 GCA TCC AGG GAT STA AGG CCA GTG GAC Nanostring Ly6c2 209GTC CTT CCA ATG 210 CCT CCA GGG CCA STA ACC CCC AGA ATA G NanostringMina 211 GTC TGC CGG AGC 212 TAA TGT GGA GGG STA ATC AGT AGG CCCNanostring Nampt 213 CAA GGA GAT GGC 214 TGG GAT CAG CAA STA GTG GAT CTGGGT Nanostring Nkg7 215 TGG CCC TCT GGT 216 TTT CAT ACT CAG STA CTC AACCCC GAC G Nanostring Hif1a 217 AAG AAC TTT TGG 218 GCA CTG TGG CTG STAGCC GCT GGA GTT Nanostring Ifih1 219 GCT GAA AAC CCA 220 ACT TCA CTG CTGSTA AAA TAC GA TGC CCC Nanostring Il17a 221 ATC AGG ACG CGC 222 GAC GTGGAA CGG STA AAA CAT TTG AGG Nanostring Il23r 223 CAC TGC AAG GCA 224 CGTTTG GTT TGT STA GCA GG TGT TGT TTT G Nanostring Il6st 225 TCG GAC GGCAAT 226 GTT GCT GGA GAT STA TTC ACT GCT GGG Nanostring Irf9 227 ACT GATCGT CGC 228 TTG GTC TGT CTT STA GTC TCC CCA AGT GCT Nanostring Kcmf1 229CTG ACC ACC CGA 230 TCC AGG TAA CGC STA TGC AGT TGC ACA Nanostring Lamp2231 GGC TGC AGC TGA 232 AAG CTG AGC CAT STA ACA TCA TAG CCA AANanostring Maf 233 AGG CAG GAG GAT 234 TCA TGG GGG TGG STA GGC TTC AGGAC Nanostring Mkln1 235 GGT TTG CCC ATC 236 GGA TCC ATT TGG STA AAC TCGGCC TTT Nanostring Ncf1 237 GCA AAG GAC AGG 238 TTT GAC ACC CTC STA ACTGGG CCC AAA Nanostring Notch1 239 GCA GGC AAA TGC 240 GTG GCC ATT GTGSTA CTC AAC CAG ACA Nanostring Hip1r 241 CTC GAG CAG CTG 242 CCA GCA GGGACC STA GGA CC CTC TTT Nanostring Ifit1 243 TCA TTC GCT ATG 244 GGC CTGTTG TGC STA CAG CCA CAA TTC Nanostring Il17f 245 AAG AAC CCC AAA 246 CAGCGA TCT CTG STA GCA GGG AGG GGA Nanostring Il24 247 TCT CCA CTC TGG 248CTG CAT CCA GGT STA CCA ACA CAG GAG A Nanostring Il7r 249 TGG CCT AGTCTC 250 CGA GCG GTT TGC STA CCC GAT ACT GT Nanostring Isg20 251 CTG TGGAAG ATG 252 GTG GTT GGT GGC STA CCA GGG AGT GGT Nanostring Khdrbs1 253GTT CGT GGA ACC 254 TCC CCT TGA CTC STA CCA GTG TGG CTG NanostringLgals3bp 255 GGC CAC AGA GCT 256 CCA GCT CAC TCT STA TCA GGA TGG GGANanostring Maff 257 TCT GAC TCT TGC 258 TGG CAC AAT CCA STA AGG CCC AAGCCT Nanostring Mt1 259 ACT ATG CGT GGG 260 GCA GGA GCT GGT STA CTG GAGGCA AGT Nanostring Ncoa1 261 GCC TCC AGC CCA 262 TGA GGG ATT TAT STA TCCTAT TCG GGG A Nanostring Notch2 263 TAC GAG TGC ACC 264 GCA GCG TCC TGGSTA TGC CAA AAT GTC Nanostring Hsbp1 265 ATC ACG TGA CCA 266 CTC TGA TACCCT STA CAG CCC GCC GGA Nanostring Ifng 267 TCT GGG CTT CTC 268 TCC TTTTGC CAG STA CTC CTG TTC CTC C Nanostring Il17ra 269 GGG GCT GAG CTG 270TGG TGT TCA GCT STA CAG AGT GCA GGA Nanostring Il27ra 271 AAG GCT GGCCTC 272 GGG CAG GGA ACC STA GAA CTT AAA CTT Nanostring Il9 273 TGG TGACAT ACA 274 TGT GTG GCA TTG STA TCC TTG CC GTC AGC Nanostring Itga3 275GCT TCA CCC AGA 276 CCC ATA TGT TGG STA ACA CCG TGC CGT Nanostring Kif2a277 TGC CGA ATA CAC 278 TCC GCC GGT TCT STA CAA GCA TTA CAA NanostringLif 279 GGG GCA GGT AGT 280 TCG GGA TCA AGG STA TGC TCA ACA CAG ANanostring Map3k5 281 CCA TCT TGG AGT 282 GCT CAG TCA GGC STA GCG AGA ACCT TCA Nanostring Mt2 283 TGT GCT GGC CAT 284 AGG CAC AGG AGC STA ATCCCT AGT TGG Nanostring Nfatc2 285 AGC TCC ACG GCT 286 CGT TTC GGA GCTSTA ACA TGG TCA GGA Nanostring Nr3c1 287 CAA GTG ATT GCC 288 CAT TGG TCATAC STA GCA GTG ATG CAG GG Nanostring Icos 289 CGG CCG ATC ATA 290 TTCCCT GGG AGC STA GGA TGT TGT CTG Nanostring Ifngr2 291 CGA AAC AAC AGC292 CGG TGA ACC GTC STA AAA TGC C CTT GTC Nanostring Il1r1 293 ACC CGAGGT CCA 294 TCT CAT TCC GAG STA GTG GTA GGC TCA Nanostring Il2ra 295 TGCAAG AGA GGT 296 GTT CCC AAG GAG STA TTC CGA GTG GCT Nanostring Inhba 297AGC AGA AGC ACC 298 TCC TGG CAC TGC STA CAC AGG TCA CAA Nanostring Itgb1299 TGG AAA ATT CTG 300 TTG GCC CTT GAA STA CGA GTG TG ACT TGGNanostring Klf10 301 CCC TCC AAA AGG 302 GGC AAA AAC AAA STA GCC TAA GTCCCC A Nanostring Litaf 303 AGT GCA CAG AAG 304 CCA GCA AAT GGA STA GGCTGC GAA ATG G Nanostring Max 305 AGG ACG CCT GCT 306 GCT GCA AAT CTG STACTA CCA TCC CCA Nanostring Mta3 307 CGG AGA AGC AGA 308 ACT TTG GGC CCASTA AGC ACC CTC TGA Nanostring Nfe2l2 309 GCC GCT TAG AGG 310 TGC TCCAGC TCG STA CTC ATC ACA ATG Nanostring Nudt4 311 TGG GGT GCC ATC 312 ATTCCA CAT GGC STA CAG TAT TTT GGC Nanostring Id2 313 TCA GCC ATT TCA 314TAA CGT TTT CGC STA CCA GGA G TCC CCA Nanostring Ikzf4 315 GGG GTC TAGCCC 316 GCC GGG GAG AGA STA AAT TCC GGT TAG Nanostring Il1rn 317 TGG TAAGCT TTC 318 TCA TCA CAT CAG STA CTT CTT TCC GAA GGG C Nanostring Il2rb319 GCA CCC CAT CCT 320 CAA GTC CAG CTC STA CAG CTA GGT GGT NanostringIrf1 321 TAA GCA CGG CTG 322 CAG CAG AGC TGC STA GGA CAT CCT TGTNanostring Jak3 323 CTC CCC AGC GAT 324 CAG CCC AAA CCA STA TGT CAT GTCAGG Nanostring Klf6 325 GAG CGG GAA CTC 326 GGG AAA ATG ACC STA AGG ACCACT GCG Nanostring Lmnb1 327 TGC CCT AGG GGA 328 CAA GCG GGT CTC STA CAAAAA ATG CTT Nanostring Mbn13 329 TGG AGC ATG AAT 330 TGA GGG TCC CAT STACCA CAC C GAG TGG Nanostring Mxi1 331 CTC AGG AGA TGG 332 CCT CGT CACTCC STA AGC GGA CGA CAC Nanostring Nfil3 333 CAC GGT GGT GAA 334 GAA AGGAGG GAG STA GGT TCC GGA GGA Nanostring Oas2 335 TGC CTG TGC TTG 336 GAAGAA GGG CCA STA CTC TGA GAA GGG Nanostring Id3 337 CCG AGG AGC CTC 338GTC TGG ATC GGG STA TTA GCC AGA TGC Nanostring Il10 339 ACT GCC TTC AGC340 CAG CTT CTC ACC STA CAG GTG CAG GGA Nanostring Il21 341 CCT GGA GTGGTA 342 TGC GTT GGT TCT STA TCA TCG C GAT TGT G Nanostring Il3 343 CACACC ATG CTG 344 CTC CTT GGC TTT STA CTC CTG CCA CGA Nanostring Irf4 345CAG AGA AAC GCA 346 AGT CCA CCA GCT STA TTC CTG G GGC TTT T NanostringJun 347 TAT TGG CCG GCA 348 GCC TGG CAC TTA STA GAC TTT CAA GCCNanostring Klf9 349 AGG GAA GGA AGA 350 TGG CCA TGT AAA STA CGC CAC AGCCAA A Nanostring Lrrfip1 351 GTC TCC AAC GCC 352 ATC TCT TCC CTT STA CAGCTA TGC CGC Nanostring Med24 353 ACT GCT AGG GGT 354 TGA GCC ATA GGT STACCT GGG CTG GGC Nanostring Myd88 355 GAA GCT GTT TGG 356 TCA TTC CTC CCCSTA CTT CGC CAG ACA Nanostring Nflcbie 357 TCG AGG CGC TCA 358 CGG ACAACA TCT STA CAT ACA GGC TGA Nanostring Pcbp2 359 CTC AAC TGA GCG 360 AGGGTT GAG GCA STA GGC AAT CAT GGA Nanostring Ier3 361 CCT TCT CCA GCT 362CCT CTT GGC AAT STA CCC TCC GTT GGG Nanostring Il10ra 363 GTA AAG GCCGGC 364 TTT CCA GTG GAG STA TCC AGT GAT GTG C Nanostring Il21r 365 AGGTCT GGC CAC 366 GGC CAC AGT CAC STA AAC ACC GTT CAA Nanostring Il4 367AGG GCT TCC AAG 368 TGC TCT TTA GGC STA GTG CTT TTT CCA GG NanostringIrf7 369 GAG GCT GAG GCT 370 ATC CTG GGG ACA STA GCT GAG CAC CCTNanostring Kat2b 371 GGT GCT TTG AGC 372 GCC CTG CAC AAG STA AGT TCT GACAA AGT Nanostring Klrd1 373 GCC TGG CTA TGG 374 CCG TGG ACC TTC STA GAGGAT CTT GTC Nanostring Lsp1 375 CCT GAG CCC TAC 376 GGG CAG CTC TAT STACAC CAA GGA GGG Nanostring Mgll 377 CGC GCA GTA GTC 378 AAG ATG AGG GCCSTA TGG CTC TTG GGT Nanostring Myst4 379 CAA CAA AGG GCA 380 TTC AAC ACAAGG STA GCA AGC GCA GAG G Nanostring Nfkbiz 381 TTA GCT GGA TGA 382 ATGTTG CTG CTG STA GCC CCA TGG TGG Nanostring Peli2 383 GCC AGA CGG TAG 384CGT GCT GTG TAT STA TGG TGG GGC TCG Nanostring Phlda1 385 GAT GAC GGAGGG 386 GGG GTT GAG GCT STA CAA AGA GGA TCT Nanostring Prdm1 387 ACC CTGGCT ATG 388 GGG AAG CTG GAT STA CAC CTG TGA GCA Nanostring Pstpip1 389GAG AGC GAG GAC 390 CCT TCC ACA TCA STA CGA GTG CAG CCC Nanostring Rela391 TGC GAC AAG GTG 392 GAG CTC GCG ATC STA CAG AAA AGA AGG NanostringRunx3 393 GCC CCT TCC CAC 394 CTC CCC CTG CTG STA CAT TTA CTA CAANanostring Sgk1 395 GGC TAG GCA CAA 396 AGC GCT CCC TCT STA GGC AGA GGAGAT Nanostring Smox 397 ACA GCC TCG TGT 398 GGC CAT TGG CTT STA GGT GGTCTG CTA Nanostring Stat4 399 GCC TCT ATG GCC 400 ACT TCC AGG AGT STA TCACCA TGG CCC Nanostring Tbx21 401 TGG GAA GCT GAG 402 GCC TTC TGC CTT STAAGT CGC TCC ACA Nanostring Tmed7 403 TGG TTA GCG TAG 404 CCC ATG GGG ATASTA GGC AGG TGC ACT Nanostring Traf3 405 ATC TGT GGG CGC 406 GGA CTG TCAAGA STA TCT GAC TGG GGC Nanostring Vav3 407 TTC TGG CAG GGA 408 TTT GGTCCT GTG STA CGA AAC CCT TAC AA Nanostring Plac8 409 TGC TCC CCA AAA 410AGG AAT GCC GTA STA TTC CAA TCG GGT Nanostring Prf1 411 ACC AAC CAG GAC412 CCC TGT GGA CAG STA TGC TGC GAG CAC Nanostring Ptprj 413 TCA CCT GGAGCA 414 TGG TAC CAT TGG STA ATG CAA CAT CCG Nanostring Rfk 415 TTT CCCTCT TGG 416 TCC CTC CCC ACA STA TGG CCT CCA CTA Nanostring Rxra 417 TTGTTG GGC GAC 418 TGG AGA GTT GAG STA TTT TGC GGA CGA A Nanostring Skap2419 TGG GTG AAC ATT 420 AAA CAG CAA CCC STA CCT GCC TCA CCG NanostringSocs3 421 TGC AGG AGA GCG 422 GAA CTG GCT GCG STA GAT TCT TGC TTCNanostring Stat5a 423 CCT CCG CTA GAA 424 GCT CTT ACA CGA STA GCT CCCGAG GCC C Nanostring Tgfb1 425 CGC CTG AGT GGC 426 ATG TCA TGG ATG STATGT CTT GTG CCC Nanostring Tmem126a 427 CTG CTT GAA TAT 428 CCA ACT AGTGCA STA GGA TCA GCA CCC CGT Nanostring Trat1 429 CAA TGG ATG CCA 430 CCTTGC CAG TCC STA ACG TTT C CTG TGT Nanostring Vax2 431 GGC CCC CGT GGA432 CAC ACA CAC ACG STA CTA TAC CAC ACG Nanostring Plagl1 433 TTG AGACTG TAT 434 GCA GGG TCT TCA STA CCC CCA GC AAG GTC AG NanostringPricklel 435 TGG GTT TCC AGT 436 GCC TTT ATT AAA STA TGC AGT T CAC CTCCCT G Nanostring Pycr1 437 CCC TGG GTG TGT 438 AAG GGG TTG AAA STA GCAGTC GGG GTG Nanostring Rngtt 439 CCC AAA AGA CTG 440 TCC ACA GGG TAA STACAT CGG GGC TGA A Nanostring Sav1 441 CGA CCC CCA ATG 442 TAG CCC ACCCTG STA TAA GGA ATG GAA Nanostring Ski 443 GGT CCC CTG CAG 444 CTT CCGTTT TCG STA TGT CTG TGG CTG Nanostring Spp1 445 CCA TGA CCA CAT 446 CCAAGC TAT CAC STA GGA CGA CTC GGC Nanostring Stat5b 447 ACT CAG CGC CCA448 GCT CTG CAA AGG STA CTT CAG CGT TGT Nanostring Tgfb3 449 GCC AAA GTCCCC 450 AAG GAA GGC AGG STA TGG AAT AGG AGG Nanostring Tnfrsf12a 451 GGGAGC CTT CCA 452 GGC ATT ATA GCC STA AGG TGT CCT CCG Nanostring Trim24453 CGG TGG TCC TTC 454 TGC AGA GCC ATT STA GCC CAA CAC A NanostringXbp1 455 GGA CCT CAT CAG 456 GCA GGT TTG AGA STA CCA AGC TGC CCANanostring Plekhf2 457 CGG CAA TAT TGT 458 GGG CGT CTT CCC STA TAT CCAGAA ACT TTT Nanostring Prkca 459 TGC TGT CCC AGG 460 CAA ATA GCC CAG STAGAT GAT GAT ACC CA Nanostring Rab33a 461 GCT GGC TTG GCA 462 TTG ATC TTCTCG STA TCC TT CCC TCG Nanostring Rora 463 GAT GTG GCA GCT 464 TTG AAGACA TCG STA GTG TGC GGG CTC Nanostring Sema4d 465 TTC TTG GGC AGT 466TCG CGG GAT CAT STA GAA CCC CAA CTT Nanostring Slamf7 467 CTC CAT GAAGCT 468 TTG ATT ACG CAG STA CAG CCA A GTG CCA Nanostring Spry1 469 AGGACT TCC CTT 470 AGC CAG GAT TCA STA CAC GCC ACT TTG TGA Nanostring Stat6471 TGC TTT TGC CAG 472 ACG CCC AGG GAG STA TGT GAC C TTT ACA NanostringTgfbr1 473 TGA TGT CAG CTC 474 TCT GCA GCG AGA STA TGG GCA ACC AAANanostring Tnfrsf13b 475 GGA AGG CAC CAG 476 CTC GTC GCA AGC STA GGA TCTCTC TGT Nanostring Trim25 477 TCT GCC TTG TGC 478 ACG GGT GCA TCA STACTG ACA GCC TAA Nanostring Xrcc5 479 AGG GGA CCT GGA 480 GAC AAG TTG GGGSTA CTC TGG CCA ATG Nanostring Pmepa1 481 GTG ACC GCT TGA 482 GCT GTGTCG GCT STA TGG GG GAT GAA Nanostring Prkd3 483 CCT GGC CTC TCA 484 AGAGGC CTT TCA STA GTT CCA GCA GGC Nanostring Rad51ap1 485 AGC AGC CAA GTG486 TGC CAC AAG GAG STA CGG TAG AGG TCC Nanostring Rorc 487 CCT CTG ACCCGT 488 GCT TCC AGA AGC STA CTC CCT CAG GGT Nanostring Sema7a 489 ATGAAA GGC TAT 490 GTG CAC AAT GGT STA GCC CCC GGC CTT Nanostring Slc2a1491 GAC CCT GCA CCT 492 GAA GCC AGC CAC STA CAT TGG AGC AAT NanostringStard10 493 AGG ACC CAG GAG 494 ATC TCC ACA GCC STA AGT CGG TGC ACCNanostring Sufu 495 ATG GGG AGT CCT 496 TAG GCC CTG CAT STA TCT GCC CAGCTC Nanostring Tgfbr3 497 TCT GGG ATT TGC 498 GTG CAG GAA GAG STA CATCCA CAG GGA Nanostring Tnfrsf25 499 CGA GCC ATG TGG 500 GAG GCT GAG AGASTA GAA AAG TGG GCA Nanostring Trps1 501 TTG TAA CGC ACT 502 CGT GCC TTTTTG STA TTG AGA TCC GTA GCC Nanostring Zeb1 503 AAG CGC TGT GTC 504 GTGAGA TGC CCC STA CCT TTG AGT GCT Nanostring Pm1 505 AAT TTG GGT CCT 506GCT CGA GAT GCC STA CTC GGC AGT GCT Nanostring Prnp 507 CCT CCC ACC TGG508 CCG TCA CAG GAG STA GAT AGC GAC CAA Nanostring Rasgrp1 509 CAA GCATGC AAA 510 CGT TAT GAG CGG STA GTC TGA GC GGT TTG Nanostring Rpp14 511GCA GCA GTG GTC 512 TGT CAC CAA CAG STA TGG TCA GGG CTT NanostringSerpinb1a 513 CAA GGT GCT GGA 514 GCG GCC CAG GTT STA GAT GCC AGA GTTNanostring Slc6a6 515 GGT GCG TTC CTC 516 AGG CCA GGA TGA STA ATA CCGCGA TGT Nanostring Stat1 517 GAG GTA GAG GCC 518 TTT AAG CTC TGC STA TGGGGA CGC CTC Nanostring Sult2b1 519 CGA TGT CGT GGT 520 GTC CTG CTG CAGSTA CTC CCT CTC CTC Nanostring Tgif1 521 GGA CCC AGT CCA 522 CGG CAA TCAGGA STA AAC CCT CCG TAT Nanostring Tnfsf11 523 AAC AAG CCT TTC 524 AGAGAT CTT GGC STA AGG GGG CCA GCC Nanostring Tsc22d3 525 TGC CAG TGT GCT526 CTG TGC ACA AAG STA CCA GAA CCA TGC Nanostring Zfp161 527 CGC CAAGAT TTC 528 TCC CCG ATT TCT STA CGT GA TCC ACA Nanostring Pou2af1 529GCC CAC TGG CCT 530 TGG GAT ATC AAA STA TCA TTT GAA ACT GTC A NanostringProcr 531 GCC AAA ACG TCA 532 ACG GCC ACA TCG STA CCA TCC AAG AAGNanostring Rbpj 533 TCC CTT AAA ACA 534 CTT CCC CTT GAC STA GGA GCC AAAG CCA Nanostring Runx1 535 GCC TGA GAA AAC 536 CAT GTG CCT GAT STA GGTAGG G GGA TTT TT Nanostring Serpine2 537 TGA GCC ATC AAA 538 GCT TGT TCACCT STA GGC AAA GGC CC Nanostring Smad3 539 ACG TGC CCC TGT 540 GAG TGGTGG GAC STA CTG AAG AGG GC Nanostring Stat2 541 GCA ACC AGG AAC 542 TCTTCG GCA AGA STA GCA GAC ACC TGG Nanostring Tal2 543 GGT GGA GGC AGC 544CAT CCT CAT CTG STA AGA GTG GCA GGC Nanostring Tgm2 545 CAG TCT CAG TGC546 ATG TCC TCC CGG STA GAG CCA TCA TCA Nanostring Tnfsf8 547 ACG CCCCCA GAG 548 CTG GGT CAG GGG STA AAG AGT AAG GAG Nanostring Ube3a 549 TCGCAT GTA CAG 550 CTT TGG AAA CGC STA TGA AAG AAG A CTC CCT NanostringZfp238 551 GCC TTG ATT GAC 552 AAG AAA AAG GGA STA ATG GGG AAA ACA ACC ANanostring Prc1 553 TCC CAA CCC TGT 554 CAG TGT GGG CAG STA GCT CAT AACTGG Nanostring Psmb9 555 TGG TTA TGT GGA 556 GGA AGG GAC TTC STA CGC AGCTGG GGA Nanostring Rel 557 GCC CCT CTG GGA 558 GGG GTG AGT CAC STA TCAACT TGG TGG Nanostring Runx2 559 AAA TCC TCC CCA 560 TGC AGA GTT CAG STAAGT GGC GGA GGG Nanostring Sertad1 561 CTG GGT GCC TTG 562 CGC CTC ATCCAA STA GAC TTG CTC TGG Nanostring Smarca4 563 TAC CGT GCC TCA 564 CCCCGG TCT TCT STA GGG AAA GCT TTT Nanostring Stat3 565 TTC AGC GAG AGC 566AAA TGC CTC CTC STA AGC AAA CTT GGG Nanostring Tap1 567 TCT CTC TTG CCT568 GGC CCG AAA CAC STA TGG GGA CTC TCT Nanostring Timp2 569 GCT GGA CGTTGG 570 CTC ATC CGG GGA STA AGG AAA GGA GAT Nanostring Tnfsf9 571 GTTTCC CAC ATT 572 AGC CCG GGA CTG STA GGC TGC TCT ACC Nanostring Ubiad1573 TAC AGA GCG CTT 574 GCC ACC ATG CCA STA GTC CCC TGT TTT NanostringZfp281 575 CCA GAC GTA GTT 576 TGC TGC TGG CAG STA GGG CAG A TTG GTANanostring Zfp410 577 CTG AAA GAG CCT 578 CCA TCA TGC ACT STA CAC GGCCTG GGA Fluidigm B2M 579 TTC TGG TGC TTG 580 CAG TAT GTT CGG & QPCR TCTCAC TGA CTT CCC ATT C Fluidigm Aim1 581 GAC GAC TCC TTT 582 AAA TTT TCTCCA & QPCR CAG ACC AAG T TCA TAA GCA ACC Fluidigm Cd44 583 GCA TCG CGGTCA 584 CAC CGT TGA TCA & QPCR ATA GTA GG CCA GCT T Fluidigm Ifngr2 585TCC TGT CAC GAA 586 ACG GAA TCA GGA & QPCR ACA ACA GC TGA CTT GCFluidigm Il6st 587 TCC CAT GGG CAG 588 CCA TTG GCT TCA & QPCR GAA TAT AGGAA AGA GG Fluidigm Klf7 589 AAG TGT AAC CAC 590 TCT TCA TAT GGA & QPCRTGC GAC AGG GCG CAA GA Fluidigm Mt2 591 CAT GGA CCC CAA 592 AGC AGG AGCAGC & QPCR CTG CTC AGC TTT Fluidigm Nudt4 593 CTG CTG TGA GGG 594 CGAGCA GTC TGC & QPCR AAG TGT ATG A CTA GCT TT Fluidigm Pstpip1 595 AGC CCTCCT GTG 596 TGG TCT TGG GAC & QPCR GTG TGA TA TTC CAT GT Fluidigm Rxra597 GCT TCG GGA CTG 598 GCG GCT TGA TAT & QPCR GTA GCC CCT CAG TGFluidigm Sod1 599 CCA GTG CAG GAC 600 GGT CTC CAA CAT & QPCR CTC ATT TTGCC TCT CT Fluidigm Tgfb1 601 TGG AGC AAC ATG 602 CAG CAG CCG GTT & QPCRTGG AAC TC ACC AAG Fluidigm GAPDH 603 GGC AAA TTC AAC 604 AGA TGG TGATGG & QPCR GGC ACA GT GCT TCC C Fluidigm Atf4 605 ATG ATG GCT TGG 606CCA TTT TCT CCA & QPCR CCA GTG ACA TCC AAT C Fluidigm Cmtm6 607 GAT ACTGGA AAA 608 AAT GGG TGG AGA & QPCR GTC AAG TCA TCG CAA AAA TGA FluidigmIl10 609 CAG AGC CAC ATG 610 GTC CAG CTG GTC & QPCR CTC CTA GA CTT TGTTT Fluidigm Il7r 611 CGA AAC TCC AGA 612 AAT GGT GAC ACT & QPCR ACC CAAGA TGG CAA GAC Fluidigm Lamp2 613 TGC AGA ATG GGA 614 GGC ACT ATT CCG &QPCR GAT GAA TTT GTC ATC C Fluidigm Myc 615 CCT AGT GCT GCA 616 TCT TCCTCA TCT & QPCR TGA GGA GA TCT TGC TCT TC Fluidigm Pcbp2 617 CAG CAT TAGCCT 618 ATG GAT GGG TCT & QPCR GGC TCA GTA GCT CTG TT Fluidigm Rasgrp1619 GTT CAT CCA TGT 620 TCA CAG CCA TCA & QPCR GGC TCA GA GCG TGTFluidigm Satb1 621 ATG GCG TTG CTG 622 CTT CCC AAC CTG & QPCR TCT CTA GGGAT GAG C Fluidigm Stat1 623 GCA GCA CAA CAT 624 TCT GTA CGG GAT & QPCRACG GAA AA CTT CTT GGA Fluidigm Tgif1 625 CTC AGA GCA AGA 626 CGT TGATGA ACC & QPCR GAA AGC ACT G AGT TAC AGA CC Fluidigm HMBS 627 TCC CTGAAG GAT 628 AAG GGT TTT CCC & QPCR GTG CCT AC GTT TGC Fluidigm B4galt1629 GCC ATC AAT GGA 630 CAT TTG GAC GTG & QPCR TTC CCT AA ATA TAG ACATGC Fluidigm Foxo1 631 CTT CAA GGA TAA 632 GAC AGA TTG TGG & QPCR GGGCGA CA CGA ATT GA Fluidigm Il16 633 CCA CAG AAG GAG 634 GTG TTT TCC TGG& QPCR AGT CAA GGA GGA TGC T Fluidigm Irf1 635 GAG CTG GGC CAT 636 TCCATG TCT TGG & QPCR TCA CAC GAT CTG G Fluidigm Lmnb1 637 GGG AAG TTT ATT638 ATC TCC CAG CCT & QPCR CGC TTG AAG A CCC ATT Fluidigm Myd88 639 TGGCCT TGT TAG 640 AAG TAT TTC TGG & QPCR ACC GTG A CAG TCC TCC TC FluidigmPmepa1 641 GCT CTT TGT TCC 642 CTA CCA CGA TGA & QPCR CCA GCA T CCA CGATTT Fluidigm Rbpj 643 AGT CTT ACG GAA 644 CCA ACC ACT GCC & QPCR ATG AAAAAC GA CAT AAG AT Fluidigm Sema4d 645 GAC CCT GGT AAC 646 TCA CGA CGTCAT & QPCR ACC ACA GG GCC AAG Fluidigm Stat3 647 GGA AAT AAC GGT 648 CATGTC AAA CGT & QPCR GAA GGT GCT GAG CGA CT Fluidigm Timp2 649 CGT TTT GCAATG 650 GGA ATC CAC CTC & QPCR CAG ACG TA CTT CTC G Fluidigm HPRT 651TCC TCC TCA GAC 652 CCT GGT TCA TCA & QPCR CGC TTT T TCG CTA ATCFluidigm Cand1 653 GAA CTT CCG CCA 654 CTG GTA AGG CGT & QPCR GCT TCCCCA GTA ATC T Fluidigm Foxp1 655 CTG CAC ACC TCT 656 GGA AGC GGT AGT &QPCR CAA TGC AG GTA CAG AGG T Fluidigm Il17ra 657 TGG GAT CTG TCA 658ATC ACC ATG TTT & QPCR TCG TGC T CTC TTG ATC G Fluidigm Irf4 659 ACA GCACCT TAT 660 ATG GGG TGG CAT & QPCR GGC TCT CTG CAT GTA GT FluidigmLOC100048299 661 CCA GCA AGA CAT 662 GAT CTT GCC TTC & QPCR /// Max TGATGA CC TCC AGT GC Fluidigm Nampt 663 CCT GTT CCA GGC 664 TCA TGG TCT TTC& QPCR TAT TCT GTT C CCC CAA G Fluidigm Pml 665 AGG AAC CCT CCG 666 TTCCTC CTG TAT & QPCR AAG ACT ATG GGC TTG CT Fluidigm Rel 667 TTG CAG AGATGG 668 CAC CGA ATA CCC & QPCR ATA CTA TGA AGC AAA TTT TGA A FluidigmSema7a 669 GGA GAG ACC TTC 670 AAG ACA AAG CTA & QPCR CAT GTG CT TGG TCCTGG T Fluidigm Stat5a 671 AAG ATC AAG CTG 672 CAT GGG ACA GCG & QPCR GGGCAC TA GTC ATA C Fluidigm Trim25 673 CCC TAC GAC CCT 674 TGT GGC TGT GCA& QPCR AAG TCA AGC TGA TAG TG Fluidigm pgk1 675 TAC CTG CTG GCT 676 CACAGC CTC GGC & QPCR GGA TGG ATA TTT CT Fluidigm Casp6 677 TGA AAT GCT TTA678 GTG GCT TGA AGT & QPCR ACG ACC TCA G CGA CAC CT Fluidigm Hif1a 679GCA CTA GAC AAA 680 CGC TAT CCA CAT & QPCR GTT CAC CTG AGA CAA AGC AAFluidigm Il21r 681 GGA GTG ACC CCG 682 AGG AGC AGC AGC & QPCR TCA TCT TATG TGA G Fluidigm Irf8 683 GAG CCA GAT CCT 684 GGC ATA TCC GGT & QPCRCCC TGA CT CAC CAG T Fluidigm Lsp1 685 CAA AGC GAG AGA 686 AAG TGG ACTTTG & QPCR CCA GAG GA GCT TGG TG Fluidigm Nfatc2 687 GAT CGT AGG CAA 688CTT CAG GAT GCC & QPCR CAC CAA GG TGC ACA Fluidigm Pou2af1 689 CAT GCTCTG GCA 690 ACT CGA ACA CCC & QPCR AAA ATC C TGG TAT GG Fluidigm Rela691 CCC AGA CCG CAG 692 GCT CCA GGT CTC & QPCR TAT CCA T GCT TCT TFluidigm Skap2 693 GTG CTC CCG ACA 694 CCC ATT CCT CAG & QPCR AAC GTA TCCAT CTT TG Fluidigm Stat5b 695 CGA GCT GGT CTT 696 CTG GCT GCC GTG &QPCR TCA AGT CA AAC AAT Fluidigm Xbp1 697 TGA CGA GGT TCC 698 TGC AGAGGT GCA & QPCR AGA GGT G CAT AGT CTG Fluidigm PPIA 699 ACG CCA CTG TCG700 GCA AAC AGC TCG & QPCR CTT TTC AAG GAG AC Fluidigm Cd2 701 TGG GATGAC TAG 702 AGT GGA TCA TGG & QPCR GCT GGA GA GCT TTG AG Fluidigm Icos703 CGG CAG TCA ACA 704 TCA GGG GAA CTA & QPCR CAA ACA A GTC CAT GCFluidigm Il24 705 AGA ACC AGC CAC 706 GTG TTG AAG AAA & QPCR CTT CAC ACGGG CCA GT Fluidigm Khdrbs1 707 CTC GAC CCG TCC 708 TTG ACT CTC CCT &QPCR TTC ACT C TCT GAA TCT TCT Fluidigm Lta 709 TCC CTC AGA AGC 710 GAGTTC TGC TTG & QPCR ACT TGA CC CTG GGG TA Fluidigm Nfatc3 711 GGG GCA GTGAAA 712 GCT TTT CAC TAT & QPCR GCC TCT AGC CCA GGA G Fluidigm Prf1 713AAT ATC AAT AAC 714 CAT GTT TGC CTC & QPCR GAC TGG CGT GT TGG CCT AFluidigm Rora 715 TTA CGT GTG AAG 716 GGA GTA GGT GGC & QPCR GCT GCA AGATT GCT CT Fluidigm Ski 717 GAG AAA GAG ACG 718 TCA AAG CTC TTG & QPCRTCC CCA CA TAG GAG TAG AAG C Fluidigm Stat6 719 TCT CCA CGA GCT 720 GACCAC CAA GGG & QPCR TCA CAT TG CAG AGA C Fluidigm Xrcc5 721 GAA GAT CACATC 722 CAG GAT TCA CAC & QPCR AGC ATC TCC A TTC CAA CCT Fluidigm RPL13A723 ATC CCT CCA CCC 724 GCC CCA GGT AAG & QPCR TAT GAC AA CAA ACT TFluidigm Cd24a 725 CTG GGG TTG CTG 726 AGA TGT TTG GTT & QPCR CTT CTGGCA GTA AAT CTG Fluidigm Id2 727 GAC AGA ACC AGG 728 AGC TCA GAA GGG &QPCR CGT CCA AAT TCA GAT G Fluidigm Il2ra 729 TGT GCT CAC AAT 730 CTCAGG AGG AGG & QPCR GGA GTA TAA GG ATG CTG AT Fluidigm Klf10 731 AGC CAACCA TGC 732 GGC TTT TCA GAA & QPCR TCA ACT TC ATT AGT TCC ATT FluidigmMaf 733 TTC CTC TCC CGA 734 CCA CGG AGC ATT & QPCR ATT TTT CA TAA CAA GGFluidigm Nfe2l2 735 CAT GAT GGA CTT 736 CCT CCA AAG GAT & QPCR GGA GTTGC GTC AAT CAA Fluidigm Prkca 737 ACA GAC TTC AAC 738 CTG TCA GCA AGC &QPCR TTC CTC ATG GT ATC ACC TT Fluidigm Runx1 739 CTC CGT GCT ACC 740ATG ACG GTG ACC & QPCR CAC TCA CT AGA GTG C Fluidigm Slc2a1 741 ATG GATCCC AGC 742 CCA GTG TTA TAG & QPCR AGC AAG CCG AAC TGC Fluidigm Sufu 743TGT TGG AGG ACT 744 AGG CCA GCT GTA & QPCR TAG AAG ATC TAA CTC TTT GG CCFluidigm Zeb1 745 GCC AGC AGT CAT 746 TAT CAC AAT ACG & QPCR GAT GAA AAGGC AGG TG Fluidigm Ywhaz 747 AAC AGC TTT CGA 748 TGG GTA TCC GAT & QPCRTGA AGC CAT GTC CAC AAT Fluidigm Cd4 749 ACA CAC CTG TGC 750 GCT CTT GTTGGT & QPCR AAG AAG CA TGG GAA TC Fluidigm Ifi35 751 TGA GAG CCA TGT 752CTC CTG CAG CCT & QPCR CTG TGA CC CAT CTT G Fluidigm Il4ra 753 GAG TGGAGT CCT 754 CAG TGG AAG GCG & QPCR AGC ATC ACG CTG TAT C Fluidigm Klf6755 TCC CAC TTG AAA 756 ACT TCT TGC AAA & QPCR GCA CAT CA ACG CCA CTFluidigm Mina 757 GAA TCT GAG GAC 758 TGG GAA AGT ACA & QPCR CGG ATC GACA AAT CTC CA Fluidigm Notch1 759 CTG GAC CCC ATG 760 AGG ATG ACT GCA &QPCR GAC ATC CAC ATT GC Fluidigm Prkd3 761 TGG CTA CCA GTA 762 TGG TAAACG CTG & QPCR TCT CCG TGT CTG ATG TC Fluidigm Runx3 763 TTC AAC GAC CTT764 TTG GTG AAC ACG & QPCR CGA TTC GT GTG ATT GT Fluidigm Smarca4 765AGA GAA GCA GTG 766 ATT TCT TCT GCC & QPCR GCT CAA GG GGA CCT C FluidigmTap1 767 TTC CCT CAG GGC 768 CTG TCG CTG ACC & QPCR TAT GAC AC TCC TGA CFluidigm Zfp36l1 769 TTC ACG ACA CAC 770 TGA GCA TCT TGT & QPCR CAG ATCCT TAC CCT TGC Fluidigm B2M 771 TTC TGG TGC TTG 772 CAG TAT GTT CGG &QPCR TCT CAC TGA CTT CCC ATT C Fluidigm 1700097N02Rik 773 CCA GAG CTTGAC 774 TCC TTT ACA AAT & QPCR CAT CAT CAG CAT ACA GGA CTG G FluidigmArmcx2 775 CCC TTC ACC CTG 776 CTT CCT CGA ATT & QPCR GTC CTT AGG CCA GAFluidigm Ccr4 777 CTC AGG ATC ACT 778 GGC ATT CAT CTT & QPCR TTC AGA AGAGC TGG AAT CG Fluidigm Cebpb 779 TGA TGC AAT CCG 780 CAC GTG TGT TGC &QPCR GAT CAA GTC AGT C Fluidigm Emp1 781 AAG AGA GGA CCA 782 CTT TTT GGTGAC & QPCR GAC CAG CA TTC TGA GTA GAG AAT Fluidigm Ier3 783 CAG CCG AAGGGT 784 AAA TCT GGC AGA & QPCR GCT CTA C AGA TGA TGG Fluidigm Itga3 785AGG GGG AGA CCA 786 GCC ATT GGA GCA & QPCR GAG TTC C GGT CAA FluidigmLrrfip1 787 AGT CTC AGC GGC 788 GCA AAC TGG AAC & QPCR AAT ACG AG TGCAGG AT Fluidigm Nfkbiz 789 CAG CTG GGG AAG 790 GGC AAC AGC AAT & QPCRTCA TTT TT ATG GAG AAA Fluidigm Ptprj 791 CCA ATG AGA CCT 792 GTA GGAGGC AGT & QPCR TGA ACA AAA CT GCC ATT TG Fluidigm Stat4 793 CGG CAT CTGCTA 794 TGC CAT AGT TTC & QPCR GCT CAG T ATT GTT AGA AGC Fluidigm GAPDH795 GGC AAA TTC AAC 796 AGA TGG TGA TGG & QPCR GGC ACA GT GCT TCC CFluidigm Acvr1b 797 AGA GGG TGG GGA 798 TGC TTC ATG TTG & QPCR CCA AACATT GTC TCG Fluidigm Arntl 799 GCC CCA CCG ACC 800 TGT CTG TGT CCA &QPCR TAC TCT TAC TTT CTT GG Fluidigm Ccr8 801 AGA AGA AAG GCT 802 GGCTCC ATC GTG & QPCR CGC TCA GA TAA TCC AT Fluidigm Chd7 803 GAG GAC GAAGAC 804 CAG TGT ATC GCT & QPCR CCA GGT G TCC TCT TCA C Fluidigm Fas 805TGC AGA CAT GCT 806 CTT AAC TGT GAG & QPCR GTG GAT CT CCA GCA AGCFluidigm Il17f 807 CCC AGG AAG ACA 808 CAA CAG TAG CAA & QPCR TAC TTAGAA GAA A AGA CTT GAC CA Fluidigm Itgb1 809 TGG CAA CAA TGA 810 ATG TCGGGA CCA & QPCR AGC TAT CG GTA GGA CA Fluidigm Map3k5 811 CAA GAA ATT AGG812 ACA CAG GAA ACC & QPCR CAC CTG AAG C CAG GGA TA Fluidigm Notch2 813TGC CTG TTT GAC 814 GTG GTC TGC ACA & QPCR AAC TTT GAG T GTA TTT GTC ATFluidigm Rorc 815 ACC TCT TTT CAC 816 TCC CAC ATC TCC & QPCR GGG AGG ACAC ATT G Fluidigm Tgfbr1 817 CAG CTC CTC ATC 818 CAG AGG TGG CAG & QPCRGTG TTG G AAA CAC TG Fluidigm HMBS 819 TCC CTG AAG GAT 820 AAG GGT TTTCCC & QPCR GTG CCT AC GTT TGC Fluidigm Aes 821 TGC AAG CGC AGT 822 TGACGT AAT GCC & QPCR ATC ACA G TCT GCA TC Fluidigm Batf 823 AGA AAG CCGACA 824 CGG AGA GCT GCG & QPCR CCC TTC A TTC TGT Fluidigm Cd247 825 CCAGAG ATG GGA 826 AGT GCA TTG TAT & QPCR GGC AAA C ACG CCT TCC FluidigmClcf1 827 TAT GAC CTC ACC 828 GGG CCC CAG GTA & QPCR CGC TAC CT GTT CAGFluidigm Fip111 829 CGT TTC CCT ATG 830 CCC ACT GCT TGG & QPCR GCA ATGTC TGG TGT Fluidigm Il1r1 831 TTG ACA TAG TGC 832 TCG TAT GTC TTT & QPCRTTT GGT ACA GG CCA TCT GAA GC Fluidigm Jun 833 CCA GAA GAT GGT 834 CTGACC CTC TCC & QPCR GTG GTG TTT CCT TGC Fluidigm Mbnl3 835 GCC AAG AGTTTG 836 CTT GCA GTT CTC & QPCR CCA TGT G ACG AGT GC Fluidigm Nr3c1 837TGA CGT GTG GAA 838 CAT TTC TTC CAG & QPCR GCT GTA AAG T CAC AAA GGTFluidigm Rpp14 839 GGA ACG CGG TTA 840 CAT CTT CCA ACA & QPCR TTC CAG TTGG ACA CCT Fluidigm Tmem126a 841 TAG CGA AGG TTG 842 GGT TTA TGA CTT &QPCR CGG TAG AC TCC ATC TTG GAC Fluidigm HPRT 843 TCC TCC TCA GAC 844CCT GGT TCA TCA & QPCR CGC TTT T TCG CTA ATC Fluidigm Ahr 845 TGC ACAAGG AGT 846 AGG AAG CTG GTC & QPCR GGA CGA TGG GGT AT Fluidigm BC021614847 CAC ATT CAA GGC 848 GTA TTG GAT TGG & QPCR TTC CTG TTT TAC AGG GTGAG Fluidigm Cd274 849 CCA TCC TGT TGT 850 TCC ACA TCT AGC & QPCR TCC TCATTG ATT CTC ACT TG Fluidigm Cmtm7 851 TCG CCT CCA TAG 852 CTC GCT AGGCAG & QPCR TGA TAG CC AGG AAG C Fluidigm Flna 853 GCA AGT GCA CAG 854TTG CCT GCT GCT & QPCR TCA CAG GT TTT GTG T Fluidigm Il2 855 GCT GTT GATGGA 856 TTC AAT TCT GTG & QPCR CCT ACA GGA GCC TGC TT Fluidigm Lad1 857CTA CAG CAG TTC 858 TGT CTT TCC TGG & QPCR CCT CAA ACG GGC TCA TFluidigm Mta3 859 CTT TGT CGT GTA 860 TTG GTA GCT GGA & QPCR TCA TTG GGTATT GTT TGC AG Fluidigm Peci 861 AAC GGT GCT GTG 862 CAG CTG GGC CAT &QPCR TTA CTG AGG TTA CTA CC Fluidigm Sap30 863 CGG TGC AGT GTC 864 CTCCCG CAA ACA & QPCR AGC TTC ACA GAG TT Fluidigm Tnfrsf12a 865 CCG CCG GAGAGA 866 CTG GAT CAG TGC & QPCR AAA GTT CAC ACC T Fluidigm pgk1 867 TACCTG CTG GCT 868 CAC AGC CTC GGC & QPCR GGA TGG ATA TTT CT FluidigmAI451617 869 CAA CTG CAG AGT 870 TGT GTC TGC CTG & QPCR /// TTG GAG GATCC TGA CT Trim30 Fluidigm Bcl11b 871 TCC CAG AGG GAA 872 CCA GAC CCTCGT & QPCR CTC ATC AC CTT CCT C Fluidigm Cd28 873 CTG GCC CTC ATC 874GGC GAC TGC TTT & QPCR AGA ACA AT ACC AAA ATC Fluidigm Ctla2b 875 GCCTCC TCT GTC 876 AAG CAG AGG ATG & QPCR AGT TGC TC AGC AGG AA FluidigmFoxp3 877 TCA GGA GCC CAC 878 TCT GAA GGC AGA & QPCR CAG TAC A GTC AGGAGA Fluidigm Il21 879 GAC ATT CAT CAT 880 TCA CAG GAA GGG & QPCR TGA CCTCGT G CAT TTA GC Fluidigm Lif 881 AAA CGG CCT GCA 882 AGC AGC AGT AAG &QPCR TCT AAG G GGC ACA AT Fluidigm Myst4 883 GCA ACA AAG GGC 884 AGA CATCTT TAG & QPCR AGC AAG GAA ACC AAG ACC Fluidigm Peli2 885 TAC ACC TTGCGA 886 GGA CGT TGG TCT & QPCR GAG ACC AG CAC TTT CC Fluidigm Sgk1 887GAT TGC CAG CAA 888 TTG ATT TGT TGA & QPCR CAC CTA TG GAG GGA CTT GFluidigm Tnfrsf25 889 CCC TGG CTT ATC 890 AGA TGC CAG AGG & QPCR CCA GACT AGT TCC AA Fluidigm PPIA 891 ACG CCA CTG TCG 892 GCA AAC AGC TCG &QPCR CTT TTC AAG GAG AC Fluidigm Aqp3 893 CTG GGG ACC CTC 894 TGG TGAGGA AGC & QPCR ATC CTT CAC CAT Fluidigm Bcl3 895 GAA CAA CAG CCT 896 TCTGAG CGT TCA & QPCR GAA CAT GG CGT TGG Fluidigm Cd74 897 GCC CTA GAG AGC898 TGG TAC AGG AAG & QPCR CAG AAA GG TAA GCA GTG G Fluidigm Ctsw 899GGT TCA ACC GGA 900 TGG GCA AAG ATG & QPCR GTT ACT GG CTC AGA C FluidigmGem 901 GAC AGC ATG GAC 902 ACG ACC AGG GTA & QPCR AGC GAC T CGC TCA TAFluidigm Il27ra 903 AGT TCC GGT ACA 904 ACA GGA GTC AGC & QPCR AGG AATGC CCA TCT GT Fluidigm Litaf 905 TCC TGT GGC AGT 906 CTA CGC AGA ACG &QPCR CTG TGT CT GGA TGA AG Fluidigm Ncfl 907 GGA CAC CTT CAT 908 CTG CCACTT AAC & QPCR TCG CCA TA CAG GAA CAT Fluidigm Plekhf2 909 GTC GGC GACTAG 910 TCC ACC ATC TTT & QPCR GAG GAC T TGC TAA TAA CC Fluidigm Smad3911 TCA AGA AGA CGG 912 CCG ACC ATC CAG & QPCR GGC AGT T TGA CCTFluidigm Tnfsf8 913 GAG GAT CTC TTC 914 TTG TTG AGA TGC & QPCR TGT ACCCTG AAA TTT GAC ACT TG Fluidigm RPL13A 915 ATC CCT CCA CCC 916 GCC CCAGGT AAG & QPCR TAT GAC AA CAA ACT T Fluidigm Arhgef3 917 GTT GGT CCC ATC918 GAT TGC TGC AGT & QPCR CTC GTG AGC TGT CG Fluidigm Bcl6 919 CTG CAGATG GAG 920 GCC ATT TCT GCT & QPCR CAT GTT GT TCA CTG G Fluidigm Cd86921 GAA GCC GAA TCA 922 CAG CGT TAC TAT & QPCR GCC TAG C CCC GCT CTFluidigm Cxcr4 923 TGG AAC CGA TCA 924 GGG CAG GAA GAT & QPCR GTG TGA GTCCT ATT GA Fluidigm Glipr1 925 TGC CCT AAT GGA 926 TTA TAT GGC CAC &QPCR GCA AAT TTT A GTT GGG TAA Fluidigm Il2rb 927 AGC ATG GGG GAG 928GGG GCT GAA GAA & QPCR ACC TTC GGA CAA G Fluidigm LOC100045833 929 TCTTGT GGC CCT 930 GCA ATG CAG AAT & QPCR /// Ly6c1 ACT GTG TG CCA TCA GA/// Ly6c2 Fluidigm Ncoa1 931 TGG CAT GAA CAT 932 GCC AAC ATC TGA & QPCRGAG GTC AG GCA TTC AA Fluidigm Prc1 933 TGG AAA CTT TTC 934 TTT CCC CCTCGG & QPCR CTA GAG TTT GAG A TTT GTA A Fluidigm Smox 935 GAT GCT TCG ACA936 GGA ACC CCG GAA & QPCR GTT CAC AGG GTA TGG Fluidigm Ubiad1 937 GTCTGG CTC CTT 938 AGT GAT GAG GAT & QPCR TCT CTA CAC AG GAC GAG GTCFluidigm Ywhaz 939 AAC AGC TTT CGA 940 TGG GTA TCC GAT & QPCR TGA AGCCAT GTC CAC AAT Fluidigm Arid5a 941 CAG AGC AGG AGC 942 GCC AAG TTC ATC& QPCR CAG AGC ATA CAC GTT C Fluidigm Casp3 943 GAG GCT GAC TTC 944 AACCAC GAC CCG & QPCR CTG TAT GCT T TCC TTT Fluidigm Cd9 945 GAT ATT CGCCAT 946 TGG TAG GTG TCC & QPCR TGA GAT AGC C TTG TAA AAC TCC FluidigmElk3 947 GAG GGG CTT TGA 948 TGT CCT GTG TGC & QPCR GAG TGC T CTG TCT TGFluidigm Golga3 949 ACA GAA AGT GGC 950 TCT CGC TGG AAC & QPCR AGA TGCAG AAT GTC AG Fluidigm Irf9 951 TGA GGC CAC CAT 952 AGC AGC AGC GAG &QPCR TAG AGA GG TAG TCT GA Fluidigm LOC100046232 953 GGA CCA GGG AGC 954GTC CGG CAC AGG & QPCR /// Nfil3 AGA ACC GTA AAT C Fluidigm Nfkbie 955CCT GGA CCT CCA 956 TCC TCT GCA ATG & QPCR ACT GAA GA TGG CAA T FluidigmPrnp 957 TCC AAT TTA GGA 958 GCC GAC ATC AGT & QPCR GAG CCA AGC CCA CATAG Fluidigm Stat2 959 GGA ACA GCT GGA 960 GTA GCT GCC GAA & QPCR ACA GTGGT GGT GGA Fluidigm Zfp161 961 GGA GTG AGG AAG 962 TGG ATT CGG GAG &QPCR TTC GGA AA TCT CCA T Fluidigm B2M 963 TTC TGG TGC TTG 964 CAG TATGTT CGG & QPCR TCT CAC TGA CTT CCC ATT C Fluidigm Abcg2 965 GCC TTG GAGTAC 966 AAA TCC GCA GGG & QPCR TTT GCA TCA TTG TTG TA Fluidigm Ccr5 967GAG ACA TCC GTT 968 GTC GGA ACT GAC & QPCR CCC CCT AC CCT TGA AAFluidigm Cxcr3 969 AGG CAG CAC GAG 970 GGC ATC TAG CAC & QPCR ACC TGATTG ACG TTC Fluidigm Fli1 971 AGA CCA TGG GCA 972 GCC CCA GGA TCT & QPCRAGA ACA CT GAT AAG G Fluidigm Gzmb 973 GCT GCT CAC TGT 974 TGG GGA ATGCAT & QPCR GAA GGA AGT TTT ACC AT Fluidigm Il10ra 975 GCT CCC ATT CCT976 AAG GGC TTG GCA & QPCR CGT CAC GTT CTG T Fluidigm Il3 977 TAC ATCTGC GAA 978 GGC TGA GGT GGT & QPCR TGA CTC TGC CTA GAG GTT FluidigmKlrd1 979 GGA TTG GAA TGC 980 TGC TCT GGC CTG & QPCR ATT ATA GTG AAA AATA ACT GAG Fluidigm Plac8 981 CAG ACC AGC CTG 982 CCA AGA CAA GTG &QPCR TGT GAT TG AAA CAA AAG GT Fluidigm Sertad1 983 TCC CTC TTC GTT 984GCT TGC GCT TCA & QPCR CTG ATT GG GAC CTT T Fluidigm Tnfsf9 985 CGC CAAGCT ACT 986 CGT ACC TCA GAC & QPCR GGC TAA AA CTT GAG ATA GGT FluidigmGAPDH 987 GGC AAA TTC AAC 988 AGA TGG TGA TGG & QPCR GGC ACA GT GCT TCCC Fluidigm Acvr2a 989 CCC TCC TGT ACT 990 GCA ATG GCT TCA & QPCR TGT TCCTAC TCA ACC CTA GT Fluidigm Ccr6 991 TTC GCC ACT CTA 992 TCT GGT GTA GAA& QPCR ATC AGT AGG AC AGG GAA GTG G Fluidigm Cxcr5 993 GAA TGA CGA CAG994 GCC CAG GTT GGC & QPCR AGG TTC CTG TTC TTA T Fluidigm Foxm1 995 ACTTTA AGC ACA 996 GGA GAG AAA GGT & QPCR TTG CCA AGC TGT GAC GAA FluidigmHip1r 997 AGT GAG CAA GCT 998 GAA GCC AGG TAC & QPCR GGA CGA C TGG GTGTG Fluidigm Il12rb1 999 CGC AGC CGA GTA 1000 AAC GGG AAA TCT & QPCR ATGTAC AAG GCA CCT C Fluidigm Il9 1001 GCC TCT GTT TTG 1002 GCA TTT TGA CGG& QPCR CTC TTC AGT T TGG ATC A Fluidigm LOC100046643 1003 TAG GTC AGATCG 1004 GTG GGG TCC TCT & QPCR /// Spry1 GGT CAT CC TTC AAG G FluidigmPrdm1 1005 TGC GGA GAG GCT 1006 TGG GTT GCT TTC & QPCR CCA CTA CGT TTGFluidigm Socs3 1007 ATT TCG CTT CGG 1008 AAC TTG CTG TGG & QPCR GAC TAGC GTG ACC AT Fluidigm Trim24 1009 ATC CAG CAG CCT 1010 GGC TTA GGG CTG &QPCR TCC ATC T TGA TTC TG Fluidigm HMBS 1011 TCC CTG AAG GAT 1012 AAGGGT TTT CCC & QPCR GTG CCT AC GTT TGC Fluidigm Anxa4 1013 TGA TGC TCTTAT 1014 CGT CTG TCC CCC & QPCR GAA GCA GGA C ATC TCT T Fluidigm Cd5l1015 GAG GAC ACA TGG 1016 ACC CTT GTG TAG & QPCR ATG GAA TGT CAC CTC CAFluidigm Daxx 1017 CAG GCC ACT GGT 1018 TCC GTC TTA CAC & QPCR CTC TCCAGT TCA AGG A Fluidigm Gap43 1019 CGG AGA CTG CAG 1020 GGT TTG GCT TCG &QPCR AAA GCA G TCT ACA GC Fluidigm Id3 1021 GAG GAG CTT TTG 1022 GCT CATCCA TGC & QPCR CCA CTG AC CCT CAG Fluidigm Il12rb2 1023 TGT GGG GTG GAG1024 TCT CCT TCC TGG & QPCR ATC TCA GT ACA CAT GA Fluidigm Inhba 1025ATC ATC ACC TTT 1026 TCA CTG CCT TCC & QPCR GCC GAG TC TTG GAA ATFluidigm Maff 1027 GAC AAG CAC GCA 1028 CAT TTT CGC AGA & QPCR CTG AGCAGA TGA CCT Fluidigm Prickle1 1029 ATG GAT TCT TTG 1030 TGA CGG TCT TGG& QPCR GCG TTG TC CTT GCT Fluidigm Spp1 1031 GGA GGA AAC CAG 1032 TGCCAG AAT CAG & QPCR CCA AGG TCA CTT TCA C Fluidigm Trps1 1033 ACT CTG CAAACA 1034 TCT TTT TCC GGA & QPCR ACA GAA GAC G CCA TAT CTG T FluidigmHPRT 1035 TCC TCC TCA GAC 1036 CCT GGT TCA TCA & QPCR CGC TTT T TCG CTAATC Fluidigm Bcl2l11 1037 GGA GAC GAG TTC 1038 AAC AGT TGT AAG & QPCRAAC GAA ACT T ATA ACC ATT TGA GG Fluidigm Cd80 1039 TCG TCT TTC ACA 1040TTG CCA GTA GAT & QPCR AGT GTC TTC AG TCG GTC TTC Fluidigm Dntt 1041 GAGCAG CAG CTC 1042 GAT GTC GCA GTA & QPCR TTG CAT AA CAA AAG CAA CFluidigm Gata3 1043 TTA TCA AGC CCA 1044 TGG TGG TGG TCT & QPCR AGC GAAG GAC AGT TC Fluidigm Ifih1 1045 CTA TTA ACC GTG 1046 CAC CTG CAA TTC &QPCR TTC AAA ACA TGA A CAA AAT CTT A Fluidigm Il15ra 1047 CCA GTG CCAACA 1048 TTG GGA GAG AAA & QPCR GTA GTG ACA GCT TCT GG Fluidigm Irf71049 CTT CAG CAC TTT 1050 TGT AGT GTG GTG & QPCR CTT CCG AGA ACC CTT GCFluidigm Mgll 1051 TCG GAA CAA GTC 1052 TCA GCA GCT GTA & QPCR GGA GGTTGC CAA AG Fluidigm Procr 1053 AGC GCA AGG AGA 1054 GGG TTC AGA GCC &QPCR ACG TGT CTC CTC Fluidigm Stard10 1055 GAG CTG CGT CAT 1056 TGC AGGCCT TGT & QPCR CAC CTA CC ACA TCT TCT Fluidigm Tsc22d3 1057 GGT GGC CCTAGA 1058 TCA AGC AGC TCA & QPCR CAA CAA GA CGA ATC TG Fluidigm pgk1 1059TAC CTG CTG GCT 1060 CAC AGC CTC GGC & QPCR GGA TGG ATA TTT CT FluidigmCasp1 1061 CCC ACT GCT GAT 1062 GCA TAG GTA CAT & QPCR AGG GTG AC AAGAAT GAA CTG GA Fluidigm Cd83 1063 TGG TTC TGA AGG 1064 CAA CCA GAG AGA &QPCR TGA CAG GA AGA GCA ACA C Fluidigm Dpp4 1065 CGG TAT CAT TTA 1066GTA GAG TGT AGA & QPCR GTA AAG AGG CAA A GGG GCA GAC C Fluidigm Gfi11067 TCC GAG TTC GAG 1068 GAG CGG CAC AGT & QPCR GAC TTT TG GAC TTC TFluidigm Ifit1 1069 TCT AAA CAG GGC 1070 GCA GAG CCC TTT & QPCR CTT GCAG TTG ATA ATG T Fluidigm Il17a 1071 CAG GGA GAG CTT 1072 GCT GAG CTT TGA& QPCR CAT CTG TGT GGG ATG AT Fluidigm Isg20 1073 TTG GTG AAG CCA 1074CTT CAG GGC ATT & QPCR GGC TAG AG GAA GTC GT Fluidigm Mt1 1075 CAC CAGATC TCG 1076 AGG AGC AGC AGC & QPCR GAA TGG AC TCT TCT TG Fluidigm Psmb91077 CGC TCT GCT GAG 1078 CTC CAC TGC CAT & QPCR ATG CTG GAT GGT TFluidigm Sult2b1 1079 ACT TCC TGT TTA 1080 AAC TCA CAG ATG & QPCR TCACCT ATG AGG A CGT TGC AC Fluidigm Vav3 1081 TTA CAC GAA GAT 1082 CAA CACTGG ATA & QPCR GAG TGC AAA TG GGA CTT TAT TCA TC Fluidigm PPIA 1083 ACGCCA CTG TCG 1084 GCA AAC AGC TCG & QPCR CTT TTC AAG GAG AC FluidigmCasp4 1085 TCC AGA CAT TCT 1086 TCT GGT TCC TCC & QPCR TCA GTG TGG A ATTTCC AG Fluidigm Creb3l2 1087 CCA GCC AGC ATC 1088 AGC AGG TTC CTG & QPCRCTC TGT GAT CTC AC Fluidigm Egr2 1089 CTA CCC GGT GGA 1090 AAT GTT GATCAT & QPCR AGA CCT C GCC ATC TCC Fluidigm Gja1 1091 TCC TTT GAC TTC 1092CCA TGT CTG GGC & QPCR AGC CTC CA ACC TCT Fluidigm Ifitm2 1093 TGG TCTGGT CCC 1094 CTG GGC TCC AAC & QPCR TGT TCA AT CAC ATC Fluidigm Il1rn1095 TGT GCC AAG TCT 1096 TTC TTT GTT CTT & QPCR GGA GAT GA GCT CAG ATCAGT Fluidigm Jak3 1097 TGG AAG ACC CGG 1098 GTC TAG CGC TGG & QPCR ATAGCA GTC CAC Fluidigm Mxi1 1099 CAA AGC CAA AGC 1100 AGT CGC CGC TTT &QPCR ACA CAT CA AAA AAC CT Fluidigm Rad51ap1 1101 AAA GCA AGA GGC 1102TGC ATT GCT GCT & QPCR CCA AGT G AGA GTT CC Fluidigm Tbx21 1103 TCA ACCAGC ACC 1104 AAA CAT CCT GTA & QPCR AGA CAG AG ATG GCT TGT G FluidigmXcl1 1105 GAG ACT TCT CCT 1106 GGA CTT CAG TCC & QPCR CCT GAC TTT CC CCACAC C Fluidigm RPL13A 1107 ATC CCT CCA CCC 1108 GCC CCA GGT AAG & QPCRTAT GAC AA CAA ACT T Fluidigm Ccl20 1109 AAC TGG GTG AAA 1110 GTC CAATTC CAT & QPCR AGG GCT GT CCC AAA AA Fluidigm Csf2 1111 GCA TGT AGA GGC1112 CGG GTC TGC ACA & QPCR CAT CAA AGA CAT GTT A Fluidigm Errfi1 1113TGC TCA GGA GCA 1114 TGG AGA TGG ACC & QPCR CCT AAC AAC ACA CTC TGFluidigm Gp49a /// 1115 TGG AGT CCT GGT 1116 TGT GTG TTC TTC & QPCRLilrb4 GTC ATT CC ACA GAA GCA TT Fluidigm Ifng 1117 ATC TGG AGG AAC 1118TTC AAG ACT TCA & QPCR TGG CAA AA AAG AGT CTG AGG TA Fluidigm Il22 ///1119 TTT CCT GAC CAA 1120 TCT GGA TGT TCT & QPCR Iltifb ACT CAG CA GGTCGT CA Fluidigm Kat2b 1121 GGA GAA ACT CGG 1122 CAG CCA TTG CAT & QPCRCGT GTA CT TTA CAG GA Fluidigm Nkg7 1123 TCT ACC TAG GCT 1124 CCG ACGGGT TCT & QPCR GGG TCT CCT ACA GTG AG Fluidigm Serpinb1a 1125 GGA TTTTCT GCA 1126 GAC AAC AGT TCT & QPCR TGC CTC TG GGG ATT TTC C FluidigmTgm2 1127 CTC ACG TTC GGT 1128 TCC CTC CTC CAC & QPCR GCT GTG ATT GTC AFluidigm Zfp238 1129 TGC ATC TGT CTC 1130 TCT GGA AAC TCC & QPCR TCT TAGTCT GCT ATA CTG TCT TCA Fluidigm Ywhaz 1131 AAC AGC TTT CGA 1132 TGG GTATCC GAT & QPCR TGA AGC CAT GTC CAC AAT Fluidigm Ccl4 1133 GCC CTC TCTCTC 1134 GAG GGT CAG AGC & QPCR CTC TTG CT CCA TTG Fluidigm Cxcl10 1135GCT GCC GTC ATT 1136 TCT CAC TGG CCC & QPCR TTC TGC GTC ATC FluidigmEtv6 1137 TCC CTT TCG CTG 1138 GGG CGT GTA TGA & QPCR TGA GAC AT AAT TCGTT Fluidigm Grn 1139 TGG CTA ATG GAA 1140 CAT CAG GAC CCA & QPCR ATT GAGGTG CAT GGT CT Fluidigm Ikzf4 1141 GCA GAC ATG CAC 1142 TGA GAG CTC CCT& QPCR ACA CCA C CTC CAG AT Fluidigm Il23r 1143 CCA AGT ATA TTG 1144 AGCTTG AGG CAA & QPCR TGC ATG TGA AGA GAT ATT GTT GT Fluidigm Klf9 1145 CTCCGA AAA GAG 1146 GCG AGA ACT TTT & QPCR GCA CAA GT TAA GGC AGT CFluidigm Phlda1 1147 CGC ACC AGC CTC 1148 TTC CGA AGT CCT & QPCR TTC ACTCAA AAC CTT Fluidigm Serpine2 1149 TTG GGT CAA AAA 1150 CCT TGA AAT ACA& QPCR TGA GAC CAG CTG CAT TAA CGA Fluidigm Tnfrsf13b 1151 GAG CTC GGGAGA 1152 TGG TCG CTA CTT & QPCR CCA CAG AGC CTC AAT Fluidigm Zfp281 1153GGA GAG GAC GGC 1154 TTT TCA TAC CCC & QPCR GTT ATT TT GGA GGA G

TABLE S6.2 RNAi sequences Duplex SEQ Catalog Gene GENE Gene ID NumberSymbol ID Accession NO: Sequence D-040676-01 Acvr2a  11480 NM_0073961155 CAAAGAAUCUAGUCUAUGA D-040676-02 Acvr2a  11480 NM_007396 1156UGACAGGACUGAUUGUAUA D-040676-03 Acvr2a  11480 NM_007396 1157GCAGAAACAUGCAGGAAUG D-040676-04 Acvr2a  11480 NM_007396 1158GGCAAUAUGUGUAAUGAAA D-044066-01 Ahr  11622 NM_013464 1159CCAAUGCACGCUUGAUUUA D-044066-02 Ahr  11622 NM_013464 1160GAAGGAGAGUUCUUGUUAC D-044066-03 Ahr  11622 NM_013464 1161CCGCAAGAUGUUAUUAAUA D-044066-04 Ahr  11622 NM_013464 1162CCAGUUCUCUUAUGAGUGC D-054696-01 Arid5a 214855 NM_145996 1163GGAAGAACGUGUAUGAUGA D-054696-02 Arid5a 214855 NM_145996 1164GAAGAGGGAUUCGCUCAUG D-054696-03 Arid5a 214855 NM_145996 1165CCUCUAAACUUCACCGGUA D-054696-04 Arid5a 214855 NM_145996 1166GGUCAUCCCUGCUUUCCCA D-040483-02 ARNTL  11865 NM_007489 1167GCAUCGAUAUGAUAGAUAA D-040483-03 ARNTL  11865 NM_007489 1168CAGUAAAGGUGGAAGAUAA D-040483-04 ARNTL  11865 NM_007489 1169GAAAUACGGGUGAAAUCUA D-040483-17 ARNTL  11865 NM_007489 1170UGUCGUAGGAUGUGACCGA D-049093-01 Batf  53314 NM_016767 1171GAACGCAGCUCUCCGCAAA D-049093-02 Batf  53314 NM_016767 1172UCAAACAGCUCACCGAGGA D-049093-03 Batf  53314 NM_016767 1173GAGGAAAGUUCAGAGGAGA D-049093-04 Batf  53314 NM_016767 1174UCAAGUACUUCACAUCAGU D-058452-01 CCR5  12774 NM_009917 1175GGAGUUAUCUCUCAGUGUU D-058452-02 CCR5  12774 NM_009917 1176UGAAGUUUCUACUGGUUUA D-058452-03 CCR5  12774 NM_009917 1177GCUAUGACAUCGAUUAUGG D-058452-04 CCR5  12774 NM_009917 1178UGAAACAAAUUGCGGCUCA D-062489-01 CCR6  12458 NM_009835 1179GCACAUAUGCGGUCAACUU D-062489-02 CCR6  12458 NM_009835 1180CCAAUUGCCUACUCCUUAA D-062489-03 CCR6  12458 NM_009835 1181GAACGGAUGAUUAUGACAA D-062489-04 CCR6  12458 NM_009835 1182UGUAUGAGAAGGAAGAAUA D-040286-04 EGR1  13653 NM_007913 1183CGACAGCAGUCCCAUCUAC D-040286-01 EGR1  13653 NM_007913 1184UGACAUCGCUCUGAAUAAU D-040286-02 EGR1  13653 NM_007913 1185ACUCCACUAUCCACUAUUA D-040286-03 EGR1  13653 NM_007913 1186AUGCGUAACUUCAGUCGUA D-040303-01 Egr2  13654 NM_010118 1187GAAGGUAUCAUCAAUAUUG D-040303-02 Egr2  13654 NM_010118 1188GAUCUCCCGUAUCCGAGUA D-040303-03 Egr2  13654 NM_010118 1189UCUCUACCAUCCGUAAUUU D-040303-04 Egr2  13654 NM_010118 1190UGACAUGACUGGAGAGAAG D-058294-01 ELK3  13713 NM_013508 1191GUAGAGAUCAGCCGGGAGA D-058294-02 ELK3  13713 NM_013508 1192GAUCAGGUUUGUGACCAAU D-058294-03 ELK3  13713 NM_013508 1193UCUUUAAUGUUGCCAAAUG D-058294-04 ELK3  13713 NM_013508 1194UGAGAUACUAUUACGACAA D-050997-21 Ets1  23871 NM_001038642 1195GCUUAGAGAUGUAGCGAUG D-050997-22 Ets1  23871 NM_001038642 1196CCUGUUACACCUCGGAUUA D-050997-23 Ets1  23871 NM_001038642 1197CAGCUACGGUAUCGAGCAU D-050997-24 Ets1  23871 NM_001038642 1198UCAAGUAUGAGAACGACUA D-040983-01 ETS2  23872 NM_011809 1199GAUCAACAGCAAUACAUUA D-040983-02 ETS2  23872 NM_011809 1200UGAAUUUGCUCAACAACAA D-040983-03 ETS2  23872 NM_011809 1201UAGAGCAGAUGAUCAAAGA D-040983-04 ETS2  23872 NM_011809 1202GAAUGACUUUGGAAUCAAG D-058395-01 Etv6  14011 NM_007961 1203GAACAAACAUGACCUAUGA D-058395-02 Etv6  14011 NM_007961 1204CAAAGAGGAUUUCCGCUAC D-058395-03 Etv6  14011 NM_007961 1205GCAUUAAGCAGGAACGAAU D-058395-04 Etv6  14011 NM_007961 1206CGCCACUACUACAAACUAA D-045283-04 Fas  14102 NM_007987 1207GAGUAAAUACAUCCCGAGA D-045283-03 Fas  14102 NM_007987 1208GGAGGCGGGUUCAUGAAAC D-045283-02 Fas  14102 NM_007987 1209CGCAGAACCUUAGAUAAAU D-045283-01 Fas  14102 NM_007987 1210GUACCAAUCUCAUGGGAAG D-041127-01 Foxo1  56458 NM_019739 1211GAAGACACCUUUACAAGUG D-041127-02 Foxo1  56458 NM_019739 1212GGACAACAACAGUAAAUUU D-041127-03 Foxo1  56458 NM_019739 1213GGAGAUACCUUGGAUUUUA D-041127-04 Foxo1   56458 NM_019739 1214GAAAUCAGCAAUCCAGAAA D-040670-01 GATA3  14462 NM_008091 1215GAAGAUGUCUAGCAAAUCG D-040670-02 GATA3  14462 NM_008091 1216CGGAAGAUGUCUAGCAAAU D-040670-03 GATA3  14462 NM_008091 1217GUACAUGGAAGCUCAGUAU D-040670-04 GATA3  14462 NM_008091 1218AGAAAGAGUGCCUCAAGUA D-060495-01 Id2  15902 NM_010496 1219CAUCUGAAUUCCCUUCUGA D-060495-02 Id2  15902 NM_010496 1220GAACACGGACAUCAGCAUC D-060495-03 Id2  15902 NM_010496 1221GUCGAAUGAUAGCAAAGUA D-060495-04 Id2  15902 NM_010496 1222CGGUGAGGUCCGUUAGGAA D-051517-01 Ikzf4  22781 NM_011772 1223GAUGGUGCCUGACUCAAUG D-051517-02 Ikzf4  22781 NM_011772 1224CGACUGAACGGCCAACUUU D-051517-03 Ikzf4  22781 NM_011772 1225GUGAAGGCCUUUAAGUGUG D-051517-04 Ikzf4  22781 NM_011772 1226GAACUCACACCUGUCAUCA D-040810-01 IL17RA  16172 NM_008359 1227GGACAGAUUUGAGGAGGUU D-040810-02 IL17RA  16172 NM_008359 1228GAAUAGUACUUGUCUGGAU D-040810-03 IL17RA  16172 NM_008359 1229UCUGGGAGCUCGAGAAGAA D-040810-04 IL17RA  16172 NM_008359 1230GAGAGCAACUCCAAAAUCA D-040007-04 IL6ST  16195 NM_010560 1231GUCCAGAGAUUUCACAUUU D-040007-03 IL6ST  16195 NM_010560 1232AGACUUACCUUGAAACAAA D-040007-02 IL6ST  16195 NM_010560 1233GAACUUCACUGCCAUUUGU D-040007-01 IL6ST  16195 NM_010560 1234GCACAGAGCUGACCGUGAA D-057981-04 IL7R  16197 NM_008372 1235GGAUUAAACCUGUCGUAUG D-057981-03 IL7R  16197 NM_008372 1236UAAGAUGCCUGGCUAGAAA D-057981-02 IL7R  16197 NM_008372 1237GCAAACCGCUCGCCUGAGA D-057981-01 IL7R  16197 NM_008372 1238GAAAGUCGUUUAUCGCAAA D-043796-04 IRF4  16364 NM_013674 1239CCAUAUCAAUGUCCUGUGA D-043796-03 IRF4  16364 NM_013674 1240CGAGUUACCUGAACACGUU D-043796-02 IRF4  16364 NM_013674 1241UAUCAGAGCUGCAAGUGUU D-043796-01 IRF4  16364 NM_013674 1242GGACACACCUAUGAUGUUA D-040737-01 Irf8  15900 NM_008320 1243GGACAUUUCUGAGCCAUAU D-040737-02 Irf8  15900 NM_008320 1244GAGCGAAGUUCCUGAGAUG D-040737-03 Irf8  15900 NM_008320 1245GCAAGGGCGUGUUCGUGAA D-040737-04 Irf8  15900 NM_008320 1246GCAACGCGGUGGUGUGCAA D-042246-04 ITGA3  16400 NM_013565 1247GCGAUGACUGGCAGACAUA D-042246-03 ITGA3  16400 NM_013565 1248GAGUGGCCCUAUGAAGUUA D-042246-02 ITGA3  16400 NM_013565 1249GGACAAUGUUCGCGAUAAA D-042246-01 ITGA3  16400 NM_013565 1250CCAGACACCUCCAACAUUA D-043776-01 Jun  16476 NM_010591 1251GAACAGGUGGCACAGCUUA D-043776-02 Jun  16476 NM_010591 1252GAAACGACCUUCUACGACG D-043776-03 Jun  16476 NM_010591 1253CCAAGAACGUGACCGACGA D-043776-04 Jun  16476 NM_010591 1254GCCAAGAACUCGGACCUUC D-041158-04 JUNB  16477 NM_008416 1255CAACCUGGCGGAUCCCUAU D-041158-03 JUNB  16477 NM_008416 1256CAACAGCAACGGCGUGAUC D-041158-02 JUNB  16477 NM_008416 1257UGGAACAGCCUUUCUAUCA D-041158-01 JUNB  16477 NM_008416 1258ACACCAACCUCAGCAGUUA D-049885-01 Kat2b  18519 NM_020005 1259GCAGUAACCUCAAAUGAAC D-049885-02 Kat2b  18519 NM_020005 1260UCACAUAUGCAGAUGAGUA D-049885-03 Kat2b  18519 NM_020005 1261GAAGAACCAUCCAAAUGCU D-049885-04 Kat2b  18519 NM_020005 1262AAACAAGCCCAGAUUCGAA D-047145-02 LRRFIP1  16978 NM_001111312 1263GAAGGGCUCCCGUAACAUG D-047145-17 LRRFIP1  16978 NM_001111312 1264AAAGAGGCCCUGCGGCAAA D-047145-18 LRRFIP1  16978 NM_001111312 1265GCUCGAGAGAUCCGGAUGA D-047145-19 LRRFIP1  16978 NM_001111312 1266AGACACAGUAAAUGACGUU D-063455-01 Mina  67014 NM_025910 1267GUAAACAGUUGCCAAGGUU D-063455-02 Mina  67014 NM_025910 1268GCACCUACCAGAACAAUUC D-063455-03 Mina  67014 NM_025910 1269GAAAUGGAACGGAGACGAU D-063455-04 Mina  67014 NM_025910 1270GGUCACCAAUUCGUGUUAA D-040813-01 MYC  17869 NM_010849 1271GACGAGACCUUCAUCAAGA D-040813-02 MYC  17869 NM_010849 1272GACAGCAGCUCGCCCAAAU D-040813-03 MYC  17869 NM_010849 1273GAAUUUCUAUCACCAGCAA D-040813-04 MYC  17869 NM_010849 1274GUACAGCCCUAUUUCAUCU D-063057-04 MYD88  17874 NM_010851 1275GAUGAUCCGGCAACUAGAA D-063057-03 MYD88  17874 NM_010851 1276GUUAGACCGUGAGGAUAUA D-063057-02 MYD88  17874 NM_010851 1277CGACUGAUUCCUAUUAAAU D-063057-01 MYD88  17874 NM_010851 1278GCCUAUCGCUGUUCUUGAA D-041128-01 NCOA1  17977 NM_010881 1279GAACAUGAAUCCAAUGAUG D-041128-02 NCOA1  17977 NM_010881 1280GAACAUGGGAGGACAGUUU D-041128-03 NCOA1  17977 NM_010881 1281UCAAGAAUCUGCUACCAAA D-041128-04 NCOA1  17977 NM_010881 1282CCAAGAAGAUGGUGAAGAU D-047764-01 Nfkb1  18033 NM_008689 1283GACAUGGGAUUUCAGGAUA D-047764-02 Nfkb1  18033 NM_008689 1284GGAUUUCGAUUCCGCUAUG D-047764-03 Nfkb1  18033 NM_008689 1285CUACGGAACUGGGCAAAUG D-047764-04 Nfkb1  18033 NM_008689 1286GGAAACGCCAGAAGCUUAU D-041110-01 NOTCH1  18128 NM_008714 1287GAACAACUCCUUCCACUUU D-041110-02 NOTCH1  18128 NM_008714 1288GGAAACAACUGCAAGAAUG D-041110-03 NOTCH1  18128 NM_008714 1289GAACCAGGCUACACAGGAA D-041110-04 NOTCH1  18128 NM_008714 1290GAAGGUGUAUACUGUGAAA D-045970-01 Nr3c1  14815 NM_008173 1291GAUCGAGCCUGAGGUGUUA D-045970-02 Nr3c1  14815 NM_008173 1292UUACAAAGAUUGCAGGUAU D-045970-03 Nr3c1  14815 NM_008173 1293GCCAAGAGUUAUUUGAUGA D-045970-04 Nr3c1  14815 NM_008173 1294GCAUGUAUGACCAAUGUAA D-048514-04 PML  18854 NM_008884 1295GCGCAAGUCCAAUAUCUUC D-048514-03 PML  18854 NM_008884 1296AGUGGUACCUCAAGCAUGA D-048514-02 PML  18854 NM_008884 1297GCGCAGACAUUGAGAAGCA D-048514-01 PML  18854 NM_008884 1298CAGCAUAUCUACUCCUUUA D-048879-01 POU2AF1  18985 NM_011136 1299GAAGAAAGCGUGGCCAUAC D-048879-02 POU2AF1  18985 NM_011136 1300CGGAGUAUGUGUCCCAUGA D-048879-03 POU2AF1  18985 NM_011136 1301UCACUAAUGUCACGCCAAG D-048879-04 P0U2AF1  18985 NM_011136 1302GCAACACGUACGAGCUCAA D-043069-09 Prdm1  12142 NM_007548 1303GGAGAGACCCACCUACAUA D-043069-10 Prdm1  12142 NM_007548 1304GCAAUACAGUAGUGAGAAA D-043069-11 Prdm1  12142 NM_007548 1305GGAAGGACAUCUACCGUUC D-043069-21 Prdm1  12142 NM_007548 1306GUACAUACAUAGUGAACGA D-042664-04 PROCR  19124 NM_011171 1307UAUCUGACCCAGUUCGAAA D-042664-03 PROCR  19124 NM_011171 1308UAACUCCGAUGGCUCCCAA D-042664-02 PROCR  19124 NM_011171 1309GUAAGUUUCCGGCCAAAGA D-042664-01 PROCR  19124 NM_011171 1310CCAAACAGGUCGCUCUUAC D-042742-01 Rbpj  19664 NM_001080928 1311CCAAACGACUCACUAGGGA D-042742-02 Rbpj  19664 NM_001080928 1312UCUCAACCCUGUGCGUUUA D-042742-03 Rbpj  19664 NM_001080928 1313GCAGACGGCAUUACUGGAU D-042742-04 Rbpj  19664 NM_001080928 1314GUAGAAGCCGAAACAAUGU D-040776-01 Rela  19697 NM_009045 1315GGAGUACCCUGAAGCUAUA D-040776-02 Rela  19697 NM_009045 1316GAAGAAGAGUCCUUUCAAU D-040776-03 Rela  19697 NM_009045 1317UAUGAGACCUUCAAGAGUA D-040776-04 Rela  19697 NM_009045 1318GAAUCCAGACCAACAAUAA D-042209-01 Rorc  19885 NM_011281 1319UGAGUAUAGUCCAGAACGA D-042209-02 Rorc  19885 NM_011281 1320CAAUGGAAGUCGUCCUAGU D-042209-03 Rorc  19885 NM_011281 1321GAGUGGAACAUCUGCAAUA D-042209-04 Rorc  19885 NM_011281 1322GCUCAUCAGCUCCAUAUUU D-048982-01 RUNX1  12394 NM_001111022 1323UGACCACCCUGGCGAGCUA D-048982-02 RUNX1  12394 NM_001111022 1324GCAACUCGCCCACCAACAU D-048982-03 RUNX1  12394 NM_001111022 1325GAGCUUCACUCUGACCAUC D-048982-04 RUNX1  12394 NM_001111022 1326ACAAAUCCGCCACAAGUUG D-045547-01 Satb1  20230 NM_009122 1327CAAAGGAUAUGAUGGUUGA D-045547-02 Satb1  20230 NM_009122 1328GAAACGAGCCGGAAUCUCA D-045547-03 Satb1  20230 NM_009122 1329GAAGGGAGCACAGACGUUA D-045547-04 Satb1  20230 NM_009122 1330GCACGCGGAAUUUGUAUUG D-042265-01 SKI  20481 NM_011385 1331GACCAUCUCUUGUUUCGUG D-042265-02 SKI  20481 NM_011385 1332GGAAAGAGAUUGAGCGGCU D-042265-03 SKI  20481 NM_011385 1333GCUGGUUCCUCCAAUAAGA D-042265-04 SKI  20481 NM_011385 1334UGAAGGAGAAGUUCGACUA D-040687-04 SMAD4  17128 NM_008540 1335GAAGGACUGUUGCAGAUAG D-040687-03 SMAD4  17128 NM_008540 1336GCAAAGGAGUGCAGUUGGA D-040687-02 SMAD4  17128 NM_008540 1337GAAGUAGGACUGCACCAUA D-040687-01 SMAD4  17128 NM_008540 1338AAAGAGCAAUUGAGAGUUU D-041135-01 Smarca4  20586 NM_011417 1339GGUCAACGGUGUCCUCAAA D-041135-02 Smarca4  20586 NM_011417 1340GAUAAUGGCCUACAAGAUG D-041135-03 Smarca4  20586 NM_011417 1341GAGCGAAUGCGGAGGCUUA D-041135-04 Smarca4  20586 NM_011417 1342CAACGGGCCUUUCCUCAUC D-051590-01 SMOX 228608 NM_145533 1343GCACAGAGAUGCUUCGACA D-051590-02 SMOX 228608 NM_145533 1344CCACGGGAAUCCUAUCUAU D-051590-03 SMOX 228608 NM_145533 1345AGAAUGGCGUGGCCUGCUA D-051590-04 SMOX 228608 NM_145533 1346UGAGGAAUUCAGCGAUUUA D-043282-01 Sp4  20688 NM_009239 1347GGACAACAGCAGAUUAUUA D-043282-02 Sp4  20688 NM_009239 1348GACAAUAGGUGCUGUUAGU D-043282-03 Sp4  20688 NM_009239 1349AAUUAGACCUGGCGUUUCA D-043282-04 Sp4  20688 NM_009239 1350GGAGUUCCAGUAACAAUCA D-061490-01 Tgif1  21815 NM_009372 1351GCAAAUAGCACCCAGCAAC D-061490-02 Tgif1  21815 NM_009372 1352CAAACGAGCGGCAGAGAUG D-061490-03 Tgif1  21815 NM_009372 1353UCAGUGAUCUGCCAUACCA D-061490-04 Tgif1  21815 NM_009372 1354GCCAAGAUUUCAGAAGCUA D-047483-04 TRIM24  21848 NM_145076 1355AAACUGACCUGUCGAGACU D-047483-03 TRIM24  21848 NM_145076 1356CCAAUACGUUCACCUAGUG D-047483-02 TRIM24  21848 NM_145076 1357GAUCAGCCUAGCUCAGUUA D-047483-01 TRIM24  21848 NM_145076 1358GCAAGCGGCUGAUUACAUA D-065500-01 TRPS1  83925 NM_032000 1359GCAAAUGGCGGAUAUGUAU D-065500-02 TRPS1  83925 NM_032000 1360GCGAGCAGAUUAUUAGAAG D-065500-03 TRPS1  83925 NM_032000 1361CUACGGUUCUGGAGUAAAU D-065500-04 TRPS1  83925 NM_032000 1362GAAGUUCGAGAGUCAAACA D-055209-02 Tsc22d3  14605 NM_010286 1363GUGAGCUGCUUGAGAAGAA D-055209-17 Tsc22d3  14605 NM_010286 1364CUGUACGACUCCAGGAUUU D-055209-18 Tsc22d3  14605 NM_010286 1365CUAUAUAGCCAUAAUGCGU D-055209-19 Tsc22d3  14605 NM_010286 1366CAGUGAGCCUGUCGUGUCA D-060426-04 UBE2B  22210 NM_009458 1367CAGAAUCGAUGGAGUCCCA D-060426-03 UBE2B  22210 NM_009458 1368GAUGGUAGCAUAUGUUUAG D-060426-02 UBE2B  22210 NM_009458 1369GGAAUGCAGUUAUAUUUGG D-060426-01 UBE2B  22210 NM_009458 1370GAAGAGAGUUUCGGCCAUU D-047149-02 VAX2  24113 NM_011912 1371GGACUUGCCUGCUGGCUAC D-047149-03 VAX2  24113 NM_011912 1372UGACACAGGUAGCGCGAGU D-047149-04 VAX2  24113 NM_011912 1373CUACAGCAGACUAGAACAA D-047149-17 VAX2  24113 NM_011912 1374GCACUGAGUUGGCCCGACA D-040825-04 XBP1  22433 NM_013842 1375UCUCAAACCUGCUUUCAUC D-040825-03 XBP1  22433 NM_013842 1376GAGUCAAACUAACGUGGUA D-040825-02 XBP1  22433 NM_013842 1377GGAUCACCCUGAAUUCAUU D-040825-01 XBP1  22433 NM_013842 1378UGACAUGUCUUCUCCACUU D-051513-01 Zeb1  21417 NM_011546 1379GAACCCAGCUUGAACGUCA D-051513-02 Zeb1  21417 NM_011546 1380GAAAGAGCACUUACGGAUU D-051513-03 Zeb1  21417 NM_011546 1381GGUUUGGUAUCUCCCAUAA D-051513-04 Zeb1  21417 NM_011546 1382GAAGUGUAUUAGCUUGAUG D-058937-01 ZFP161  22666 NM_009547 1383CCUCCGCUCUGACAUAUUU D-058937-02 ZFP161  22666 NM_009547 1384GAUUCUCGGUAUCCGGUUU D-058937-03 ZFP161  22666 NM_009547 1385CCGCCAAGAUUUCCGUGAA D-058937-04 ZFP161  22666 NM_009547 1386AAAGACCAUUUGCGUGUCA D-057818-01 ZFP281 226442 NM_177643 1387GCACCACCGCGAUGUAUUA D-057818-02 ZFP281 226442 NM_177643 1388GAACAACGUACCAGAUUGA D-057818-03 ZFP281 226442 NM_177643 1389AAGCAAGGCCCGAUAAGUA D-057818-04 ZFP281 226442 NM_177643 1390GAUCAGUACUCUGGCAAAU D-041703-01 ZFP36L1  12192 NM_007564 1391UCAAGACGCCUGCCCAUUU D-041703-02 ZFP36L1  12192 NM_007564 1392UCAGCAGCCUUAAGGGUGA D-041703-03 ZFP36L1  12192 NM_007564 1393GGAGCUGGCGAGCCUCUUU D-041703-04 ZFP36L1  12192 NM_007564 1394CGAAUCCCCUCACAUGUUU

Example 2: A Transcriptional Time Course of Th17 Differentiation

The differentiation of naïve CD4+ T-cells into Th17 cells was inducedusing TGF-β1 and IL-6, and measured transcriptional profiles usingmicroarrays at eighteen time points along a 72 hr time course during thedifferentiation of naïve CD4+ T-cells into Th17 cells, induced by acombination of the anti-inflammatory cytokine TGF-β1 and theproinflammatory cytokine IL-6 (FIG. 1, FIG. 6A, FIG. 6B and FIG. 6C, seeMethods in Example 1). As controls, mRNA profiles were measured forcells that were activated without the addition of differentiatingcytokines (Th0). 1,291 genes that were differentially expressedspecifically during Th17 differentiation were identified by comparingthe Th17 differentiating cells to the control cells (see Methods inExample 1) and partitioned into 20 co-expression clusters (k-meansclustering, see Methods in Example 1, FIG. 1b and FIG. 7) that displayeddistinct temporal profiles. These clusters were used to characterize theresponse and reconstruct a regulatory network model, as described below(FIG. 2a ).

Three Main Waves of Transcription and Differentiation:

There are three transcriptional phases as the cells transition from anaïve-like state (t=0.5 hr) to Th17 (t=72 hr; FIG. 1c and FIG. 6c ):early (up to 4 hr), intermediate (4-20 hr), and late (20-72 hr). Eachcorresponds, respectively, to a differentiation phase (Korn et al., AnnuRev Immunol 2009): (1) induction, (2) onset of phenotype andamplification, and (3) stabilization and IL-23 signaling.

The early phase is characterized by transient induction (e.g., ClusterC5, FIG. 1b ) of immune response pathways (e.g., IL-6 and TGF-βsignaling; FIG. 1d ). The first transition point (t=4 hr) is marked by asignificant increase in the expression level of ROR-γt, which is notdetectable at earlier time points. The second transition (t=20 hr) isaccompanied by significant changes in cytokine expression, withinduction of Th17 signature cytokines (e.g., IL-17) that strengthen theTh17 phenotype and a concomitant decrease in other cytokines (e.g.,IFN-γ) that belong to other T cell lineages.

Some early induced genes display sustained expression (e.g., ClusterC10, FIG. 1b ); these are enriched for transcription regulators (TRs)also referred to herein as transcription factors (TFs), including thekey Th17 factors Stat3, Irf4 and Batf, and the cytokine and receptormolecules IL-21, Lif, and Il2ra.

The transition to the intermediate phase (t=4 hr) is marked by inductionof ROR-γt (master TF; FIG. 6d ) and another 12 TFs (Cluster C20, FIG. 1b), both known (e.g., Ahr) and novel (e.g., Trps1) to Th17differentiation. At the 4 hr time point, the expression of ROR-γt, themaster TF of Th17 differentiation, significantly increases (FIG. 6d)—marking the beginning of the accumulation of differentiationphenotypes (‘intermediate phase’)—and remains elevated throughout therest of the time course. Another 12 factors show a similar pattern(Cluster 8 C20, FIG. 1b ). These include Ahr and Rbpj, as well as anumber of factors (e.g., Etv6 and Trps1) not described previously ashaving roles in Th17 differentiation. Overall, the 585 genes that areinduced between 4 and 20 hrs are differentially expressed andsubstantially distinct from the early response genes (FIG. 1b ; e.g.,clusters C20, C14, and C1).

During the transition to the late phase (t=20 hr), mRNAs of Th17signature cytokines are induced (e.g., IL-17a, IL-9; cluster C19)whereas mRNAs of cytokines that signal other T cell lineages arerepressed (e.g., IFN-γ and IL-4). Regulatory cytokines from the IL-10family are also induced (IL-10, IL-24), possibly as a self-limitingmechanism related to the emergence of ‘pathogenic’ or ‘non-pathogenic’Th17 cells (Lee et al., Induction and Molecular Signature of PathogenicTh17 Cells, Nature Immunol 13, 991-999; doi:10.1038/ni.2416). Around 48hr, the cells induce IL23r (data not shown), which plays an importantrole in the late phase (FIG. 8A, 8B).

Between 20 and 42 hrs post activation (i.e., starting 16 hrs after theinduction of ROR-γt expression), there is a substantial change comparedto Th0 in the expression of 821 genes, including many major cytokines(e.g., cluster C19, FIG. 1b ). The expression of Th17-associatedinflammatory cytokines, including IL-17a, IL-24, IL-9 and lymphotoxinalpha LTA (Elyaman, W. et al. Notch receptors and Smad3 signalingcooperate in the induction of interleukin-9-producing T cells. Immunity36, 623-634, doi:10.1016/j.immuni.2012.01.020 (2012)), is stronglyinduced (FIG. 1d ), whereas other cytokines and chemokines are repressedor remain at their low basal level (Clusters C8 and C15, FIG. 1b andFIG. 7). These include cytokines that characterize other T-helper celltypes, such as IL-2 (Th17 differentiation inhibitor), IL-4 (Th2), andIFN-γ (Th1), and others (Csf1, Tnfsf9/4 and Ccl3). Finally, regulatorycytokines from the IL-10 family are also induced (IL-10, IL-24),possibly as a self-limiting mechanism. Thus, the 20 hr time point mightbe crucial for the emergence of the proposed ‘pathogenic’ versus‘nonpathogenic’/regulatory Th17 cells (Lee et al., Nature Immunol 2012).

Most expression changes in the 1,055 genes differentially expressed inthe remainder of the time course (>48 hr) are mild, occur in genes thatresponded during the 20-42 hr period (FIG. 1, e.g., clusters C18, C19,and C20), and typically continue on the same trajectory (up or down).Among the most strongly late-induced genes is the TF Hif1a, previouslyshown to enhance Th17 development via interaction with ROR-γt (Dang, E.V. et al. Control of T(H)17/T(reg) balance by hypoxia-induciblefactor 1. Cell 146, 772-784, doi:10.1016/j.cell.2011.07.033 (2011)). Thegenes over-expressed at the latest time point (72 hr) are enriched forapoptotic functions (p<10⁻⁶), consistent with the limited survival ofTh17 cells in primary cultures, and include the Th2 cytokine IL-4 (FIG.8a ), suggesting that under TGF-β1+IL-6 treatment, the cells might havea less stable phenotype.

The peak of induction of IL-23r mRNA expression occurs at 48 hr and, atthis time point one begins to see IL-23r protein on the cell surface(data not shown). The late phase response depends in part on IL-23, asobserved when comparing temporal transcriptional profiles between cellsstimulated with TGF-β1+IL-6 versus TGF-β1+IL-6+IL-23, or between WT andIL-23r−/− cells treated with TGF-β1+IL-6+IL-23 (FIG. 8). For instance,in IL-23r-deficient Th17 cells, the expression of IL-17ra, IL-1r1,IL-21r, ROR-γt, and Hif1a is decreased, and IL-4 expression isincreased. The up-regulated genes in the IL-23r−/− cells are enrichedfor other CD4+ T cell subsets, suggesting that, in the absence of IL-23signaling, the cells start to dedifferentiate, thus further supportingthe hypothesis that IL-23 may have a role in stabilizing the phenotypeof differentiating Th17 cells.

Example 3: Inference of Dynamic Regulatory Interactions

Without wishing to be bound by any one theory, it was hypothesized thateach of the clusters (FIG. 1b ) encompasses genes that share regulatorsactive in the relevant time points. To predict these regulators, ageneral network of regulator-target associations from published genomicsprofiles was assembled (Linhart, C., Halperin, Y. & Shamir, R.Transcription factor and microRNA motif discovery: the Amadeus platformand a compendium of metazoan target sets. Genome research 18, 1180-1189,doi:10.1101/gr.076117.108 (2008); Zheng, G. et al. ITFP: an integratedplatform of mammalian transcription factors. Bioinformatics 24,2416-2417, doi:10.1093/bioinformatics/btn439 (2008); Wilson, N. K. etal. Combinatorial transcriptional control in blood stem/progenitorcells: genome-wide analysis of ten major transcriptional regulators.Cell Stem Cell 7, 532-544, doi:10.1016/j.stem.2010.07.016 (2010);Lachmann, A. et al. in Bioinformatics Vol. 26 2438-2444 (2010);Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0.Bioinformatics 27, 1739-1740, doi:10.1093/bioinformatics/btr260 (2011);Jiang, C., Xuan, Z., Zhao, F. & Zhang, M. TRED: a transcriptionalregulatory element database, new entries and other development. NucleicAcids Res 35, D137-140 (2007); Elkon, R., Linhart, C., Sharan, R.,Shamir, R. & Shiloh, Y. in Genome Research Vol. 13 773-780 (2003); Heng,T. S. & Painter, M. W. The Immunological Genome Project: networks ofgene expression in immune cells. Nat. Immunol. 9, 1091-1094,doi:10.1038/ni1008-1091 (2008)) (FIG. 2a , see Methods in Example 1)

The general network of regulator-target associations from publishedgenomics profiles was assembled as follows: in vivo protein-DNA bindingprofiles for 298 regulators (Linhart, C., Halperin, Y. & Shamir, R.Transcription factor and microRNA motif discovery: the Amadeus platformand a compendium of metazoan target sets. Genome research 18, 1180-1189,doi:10.1101/gr.076117.108 (2008); Zheng, G. et al. ITFP: an integratedplatform of mammalian transcription factors. Bioinformatics 24,2416-2417, doi:10.1093/bioinformatics/btn439 (2008); Wilson, N. K. etal. Combinatorial transcriptional control in blood stem/progenitorcells: genome-wide analysis of ten major transcriptional regulators.Cell Stem Cell 7, 532-544, doi:10.1016/j.stem.2010.07.016 (2010);Lachmann, A. et al. in Bioinformatics Vol. 26 2438-2444 (2010);Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0.Bioinformatics 27, 1739-1740, doi:10.1093/bioinformatics/btr260 (2011);Jiang, C., Xuan, Z., Zhao, F. & Zhang, M. TRED: a transcriptionalregulatory element database, new entries and other development. NucleicAcids Res 35, D137-140 (2007), 825 DNA cis-regulatory elements scored ineach gene's promoter (Elkon, R., Linhart, C., Sharan, R., Shamir, R. &Shiloh, Y. Genome-wide in silico identification of transcriptionalregulators controlling the cell cycle in human cells. Genome research13, 773-780, doi:10.1101/gr.947203 (2003)), transcriptional responses tothe knockout of 11 regulatory proteins, and regulatory relationsinferred from co-expression patterns across 159 immune cell types (Heng,T. S. & Painter, M. W. The Immunological Genome Project: networks ofgene expression in immune cells. Nat. Immunol. 9, 1091-1094,doi:10.1038/ni1008-1091 (2008)) (see Methods in Example 1). While mostprotein-DNA binding profiles were not measured in Th17 cells,DNA-binding profiles in Th17 cells of a number of key TFs, includingIrf4 and Batf (Glasmacher, E. et al. A Genomic Regulatory Element ThatDirects Assembly and Function of Immune-Specific AP-1-IRF Complexes.Science, doi:10.1126/science.1228309 (2012)), Stat3 and Stat5 (Yang, X.P. et al. Opposing regulation of the locus encoding IL-17 throughdirect, reciprocal actions of STAT3 and STAT5. Nat. Immunol. 12,247-254, doi:10.1038/ni.1995 (2011)), and Rorc (Xiao et al.,unpublished) has been included.

A regulator was then connected to a gene from its set of putativetargets only if there was also a significant overlap between theregulator's putative targets and that gene's cluster (see Methods inExample 1). Since different regulators act at different times, theconnection between a regulator and its target may be active only withina certain time window. To determine this window, each edge was labeledwith a time stamp denoting when both the target gene is regulated (basedon its expression profile) and the regulator node is expressed atsufficient levels (based on its mRNA levels and inferred protein levels(Schwanhäusser, B. et al. Global quantification of mammalian geneexpression control. Nature 473, 337-342, doi:10.1038/nature10098(2011)); see Methods in Example 1). For the target gene, the time pointsin which it is either differentially expressed compared to the Th0condition or is being induced or repressed compared with preceding timepoints in the Th17 time course were considered. For the regulator node,only time points where the regulator is sufficiently expressed and notrepressed relative to the Th0 condition were included. To this end, theregulator's predicted protein expression level was inferred from itsmRNA level using a recently proposed model (Schwanhäusser, B. et al.Global quantification of mammalian gene expression control. Nature 473,337-342, doi:10.1038/nature10098 (2011)) (see Methods in Example 1). Inthis way, a network ‘snapshot’ was derived for each of the 18 timepoints (FIG. 2b-d ). Overall, 9,159 interactions between 71 regulatorsand 1,266 genes were inferred in at least one network.

Substantial Regulatory Re-Wiring During Differentiation:

The active factors and interactions change from one network to the next.The vast majority of interactions are active only at some time window(FIG. 2c ), even for regulators (e.g., Batf) that participate in allnetworks. Based on similarity in active interactions, three networkclasses were identified (FIG. 2c ), corresponding to the threedifferentiation phases (FIG. 2d ). All networks in each phase werecollapsed into one model, resulting in three consecutive network models(FIG. 9A, 9B). Among the regulators, 33 are active in all of thenetworks (e.g. many known master regulators such as Batf1, Irf4, andStat3), whereas 18 are active in only one (e.g. Stat1 and Irf1 in theearly network; ROR-γt in the late network). Indeed, while ROR-γt mRNAlevels are induced at −4h, ROR-γt protein levels increase atapproximately 20h and further rise over time, consistent with the model(FIG. 9).

Densely Interconnected Transcriptional Circuits in Each Network:

At the heart of each network is its ‘transcriptional circuit’,connecting active TFs to target genes that themselves encode TFs. Forexample, the transcriptional circuit in the early response networkconnects 48 factors that are predicted to act as regulators to 72factors whose own transcript is up- or down-regulated during the firstfour hours (a subset of this model is shown in FIG. 2e ). The circuitautomatically highlights many TFs that were previously implicated inimmune signaling and Th17 differentiation, either as positive ornegative regulators, including Stat family members, both negative(Stat1, Stat5) and positive (Stat3), the pioneering factor Batf, TFstargeted by TGF-β signaling (Smad2, Runx1, and Irf7), several TFstargeted by TCR signaling (Rel, Nfkb1, and Jun), and several interferonregulatory factors (Irf4 and Irf1), positioned both as regulators and astarget genes that are strongly induced. In addition, 34 regulators thatwere not previously described to have a role in Th17 differentiationwere identified (e.g., Sp4, Egr2, and Smarca4). Overall, the circuit isdensely intraconnected (Novershtern et al., Cell 2011), with 16 of the48 regulators themselves transcriptionally controlled (e.g., Stat1,Irf1, Irf4, Batf). This suggests feedback circuitry, some of which maybe auto-regulatory (e.g., for Irf4, Stat3 and Stat1).

As in the early network, there is substantial cross-regulation betweenthe 64 TFs in the intermediate and late transcriptional circuits, whichinclude major Th17 regulators such as ROR-γt, Irf4, Batf, Stat3, andHif1a (FIG. 2e ).

Ranking Novel Regulators for Systematic Perturbation:

In addition to known Th17 regulators, the network includes dozens ofnovel factors as predicted regulators (FIG. 2d ), induced target genes,or both (FIG. 2E). It also contains receptor genes as induced targets,both previously known in Th17 cells (e.g., IL-1R1, IL-17RA) and novel(e.g., Fas, Itga3). This suggests substantial additional complexitycompared to current knowledge, but must be systematically tested tovalidate the role and characterize the function of each candidate.

Candidate regulators were ranked for perturbation (FIG. 2a, 3a , seeMethods in Example 1), guided by features that reflect a regulatory role(FIG. 3a , “Network Information”) and a role as target (FIG. 3a , “GeneExpression Information”).

To this end, a scoring scheme was devised to rank candidate regulatorsfor perturbation (FIG. 2a , FIG. 3a , FIG. 10, Methods), guided byprotein activity (participation as a regulator node, FIG. 3a , “NetworkInformation”) and mRNA level (changes in expression as a target, FIG. 3a, “Gene Expression Information”; Methods). Under each criterion, severalfeatures were considered for selecting genes to perturb (see Methods inExample 1). In “Network Information”, it was considered whether the geneacts as regulator in the network, the type of experimental support forthis predicted role, and whether it is predicted to target key Th17genes. In “Gene Expression Information”, it was considered changes inmRNA levels of the encoding gene in the time course data (preferringinduced genes), under IL23R knockout, or in published data ofperturbation in Th17 cells (e.g., Batf knockout (Schraml, B. U. et al.in Nature Vol. 460 405-409 (2009)); See Methods for the complete list);and whether a gene is more highly expressed in Th17 cells as compared toother CD4+ subsets, based on genome wide expression profiles (Wei, G. etal. in Immunity Vol. 30 155-167 (2009)).

The genes were computationally ordered to emphasize certain features(e.g., a predicted regulator of key Th17 genes) over others (e.g.,differential expression in the time course data). A similar scheme wasused to rank receptor proteins (see Methods in Example 1). Supportingtheir quality, the top-ranked factors are enriched (p<10⁻³) for manuallycurated Th17 regulators (FIG. 10), and correlate well (Spearman r>0.86)with a ranking learned by a supervised method (see Methods in Example1). 65 genes were chose for perturbation: 52 regulators and 13receptors. These included most of the top 44 regulators and top 9receptors (excluding a few well-known Th17 genes and/or those for whichknockout data already existed), as well as additional representativelower ranking factors.

Example 4: Nanowire-Based Perturbation of Primary T Cells

While testing the response of naïve CD4+ T cells from knock-out micedeleted for key factors is a powerful strategy, it is limited by theavailability of mouse strains or the ability to generate new ones. Inunstimulated primary mouse T cells, viral- or transfection-based siRNAdelivery has been nearly impossible because it either altersdifferentiation or cell viability (Dardalhon, V. et al.Lentivirus-mediated gene transfer in primary T cells is enhanced by acentral DNA flap. Gene therapy 8, 190-198 (2001); McManus, M. et al.Small interfering RNA-mediated gene silencing in T lymphocytes. TheJournal of Immunology 169, 5754 (2002)). a new delivery technology basedon silicon nanowires (NWs) (Shalek et al., Proc Natl Acad Sci U.S.A.2010; Shalek, A. K. et al. Nanowire-Mediated Delivery Enables FunctionalInterrogation of Primary Immune Cells: Application to the Analysis ofChronic Lymphocytic Leukemia. Nano Lett. 12, 6498-6504,doi:10.1021/nl3042917 (2012)) was, therefore, used, which was optimizedto effectively (>95%) deliver siRNA into naïve T cells withoutactivating them (FIGS. 3b and c ) (Shalek et al., Nano Lett 2012).

Recently, it was demonstrated that NWs are able to effectively penetratethe membranes of mammalian cells and deliver a broad range of exogenousmolecules in a minimally invasive, non-activating fashion (Shalek etal., Proc. Natl. Acad. Sci. U.S.A. 2010; Shalek, et al., Nano Lett.2012). In particular, the NW-T cell interface (FIG. 3b ) was optimizedto effectively (>95%) deliver siRNAs into naïve murine T cells. Thisdelivery neither activates nor induces differentiation of naïve T cellsand does not affect their response to conventional TCR stimulation withanti-CD3/CD28 (FIG. 3c ) (Shalek, et al., Nano Lett. 2012)).Importantly, NW-delivered siRNAs yielded substantial target transcriptknockdowns, prior to and even up to 48h after anti-CD3/CD28 activation,despite rapid cellular proliferation (FIG. 3d ).

It was then attempted to perturb 60 genes with NW-mediated siRNAdelivery and efficient knockdown (<60% transcript remaining at 48 hrpost activation) was achieved for 34 genes (FIG. 3d and FIG. 11, TableS6.2). Knockout mice were obtained for seven other genes, two of which(Irf8 and Il17ra) were also in the knockdown set. Altogether, 39 of the65 selected genes were successfully perturbed—29 regulators and 10receptors—including 21 genes not previously associated with Th17differentiation.

Nanowire-Based Screen Validates 39 Regulators in the Th17 Network:

the effects of the perturbation on gene expression were profiled at twotime points. 28 of the perturbations were profiled at 10 hr after thebeginning of differentiation, soon after the induction of ROR-γt (FIG.6), and all of the perturbations were profiled at 48 hr, when the Th17phenotype becomes more established (FIG. 1b ). Two of the perturbations(Il17ra and Il21r knockouts) were also profiled at 60 hr.

In particular, the effects of perturbations at 48 hr post-activation onthe expression of 275 signature genes were measured using the NanostringnCounter system (Il17ra and Il21r knockouts were also measured at 60hr).

The signature genes were computationally chosen to cover as many aspectsof the differentiation process as possible (see Methods in Example 1):they include most differentially expressed cytokines, TFs, and cellsurface molecules, as well as representatives from each cluster (FIG. 1b), enriched function, and predicted targets in each network. Forvalidation, a signature of 85 genes was profiled using the FluidigmBioMark system, obtaining highly reproducible results (FIG. 12).

The signature genes for expression analysis were computationally chosento cover as many aspects of the differentiation process as possible (seeMethods in Example 1). They include the majority of the differentiallyexpressed cytokines, TFs, and cell surface genes, as well asrepresentative genes from each expression cluster (FIG. 1b ), enrichedbiological function, and predicted targets of the regulators in eachnetwork. Importantly, since the signature includes most of the genesencoding the perturbed regulators, the connections between them (FIG. 4a, ‘perturbed’), including feedback and feed-forward loops, could bedetermined.

The statistical significance of a perturbation's effect on a signaturegene was scored by comparing to non-targeting siRNAs and to 18 controlgenes that were not differentially expressed (see Methods in Example 1,FIG. 4a , all non-grey entries are significant). Perturbation of 26 ofthe tested regulators had a significant effect on the expression of atleast 25 signature genes at the 48 hr time point (10% of signature genesthat had any response). On average, a perturbation affected 40 genes,and 80% of the signature genes were affected by at least one regulator.Supporting the original network model (FIG. 2), there is a significantoverlap between the genes affected by a regulator's knockdown and itspredicted targets (p≤0.01, permutation test; see Methods in Example 1).

To study the network's dynamics, the effect of 28 of the perturbationsat 10 hr (shortly after the induction of ROR-γt) was measured using theFluidigm Biomark system. It was found that 30% of the functionalinteractions are present with the same activation/repression logic atboth 10 hr and 48 hr, whereas the rest are present only in one timepoint (FIG. 13). This is consistent with the extent of rewiring in theoriginal model (FIG. 2b ).

Whenever possible, the function of each regulator was classified aseither positive or negative for Th17 differentiation. Specifically, atthe 48 hr time point, perturbation of 22 of the regulators significantlyattenuated IL-17A or IL-17F expression (‘Th17 positive regulators’, FIG.4b , blue) and perturbation of another five, significantly increasedIL-17 levels (‘Th17 negative regulators’, FIG. 4b , red). 12 of thesestrongly positive or negative regulators were not previously associatedwith Th17 cells (FIG. 4b , light grey halos around blue and red nodes).A color version of these figures can be found in Yosef et al., “Dynamicregulatory network controlling Th17 cell differentiation, Nature, vol.496: 461-468 (2013)/doi: 10.1038/nature11981. Next, the role of thesestrong positive and negative regulators in the development of the Th17phenotype was focused on.

Two Coupled Antagonistic Circuits in the Th17 Network:

Characterizing each regulator by its effect on Th17 signature genes(e.g. IL17A, IL17F, FIG. 4b , grey nodes, bottom), it was found that at48 hr the network is organized into two antagonistic modules: a moduleof 22 ‘Th17 positive factors’ (FIG. 4b , blue nodes: 9 novel) whoseperturbation decreased the expression of Th17 signature genes (FIG. 4b ,grey nodes, bottom), and a module of 5 ‘Th17 negative factors’ (FIG. 4b, red nodes: 3 novel) whose perturbation did the opposite. A colorversion of these figures can be found in Yosef et al., “Dynamicregulatory network controlling Th17 cell differentiation, Nature, vol.496: 461-468 (2013)/doi: 10.1038/nature11981. Each of the modules istightly intra-connected through positive, self-reinforcing interactionsbetween its members (70% of the intra-module edges), whereas most (88%)inter-module interactions are negative. This organization, which isstatistically significant (empirical p-value<10⁻³; see Methods inExample 1, FIG. 14), is reminiscent to that observed previously ingenetic circuits in yeast (Segre, D., Deluna, A., Church, G. M. &Kishony, R. Modular epistasis in yeast metabolism. Nat. Genet. 37,77-83, doi:10.1038/ng1489 (2005); Peleg, T., Yosef, N., Ruppin, E. &Sharan, R. Network-free inference of knockout effects in yeast. PLoSComput Biol 6, e1000635, doi:10.1371/journal.pcbi.1000635 (2010)). At 10hrs, the same regulators do not yield this clear pattern (p>0.5),suggesting that at that point, the network is still malleable.

The two antagonistic modules may play a key role in maintaining thebalance between Th17 and other T cell subsets and in self-limiting thepro-inflammatory status of Th17 cells. Indeed, perturbing Th17 positivefactors also induces signature genes of other T cell subsets (e.g.,Gata3, FIG. 4b , grey nodes, top), whereas perturbing Th17 negativefactors suppresses them (e.g., Foxp3, Gata3, Stat4, and Tbx21).

Example 5: Validation and Characterization of Novel Factors

The studies presented herein focused on the role of 12 of the positiveor negative factors (including 11 of the 12 novel factors that have notbeen associated with Th17 cells; FIG. 4b , light grey halos). RNA-Seqwas used after perturbing each factor to test whether its predictedtargets (FIG. 2) were affected by perturbation (FIG. 4c , Venn diagram,top). Highly significant overlaps (p<10⁻⁵) for three of the factors(Egr2, Irf8, and Sp4) that exist in both datasets were found, and aborder-line significant overlap for the fourth (Smarca4) was found,validating the quality of the edges in the network.

Next, the designation of each of the 12 factors as ‘Th17 positive’ or‘Th17 negative’ was assessed by comparing the set of genes that respondto that factor's knockdown (in RNA-Seq) to each of the 20 clusters (FIG.1b ). Consistent with the original definitions, knockdown of a ‘Th17positive’ regulator down-regulated genes in otherwise induced clusters,and up-regulated genes in otherwise repressed or un-induced clusters(and vice versa for ‘Th17 negative’ regulators; FIG. 4d and FIG. 15a,b). The genes affected by either positive or negative regulators alsosignificantly overlap with those bound by key CD4+ transcriptionregulators (e.g., Foxp3 (Marson, A. et al. Foxp3 occupancy andregulation of key target genes during T cell stimulation. Nature 445,931-935, doi:10.1038/nature05478 (2007); Zheng, Y. et al. Genome-wideanalysis of Foxp3 target genes in developing and mature regulatory Tcells. Nature 445, 936-940, doi:10.1038/nature05563 (2007)), Batf, Irf4,and ROR-γt (Glasmacher, E. et al. A Genomic Regulatory Element ThatDirects Assembly and Function of Immune-Specific AP-1-IRF Complexes.Science (New York, N.Y.), doi:10.1126/science.1228309 (2012); Ciofani,M. et al. A Validated Regulatory Network for Th17 Cell Specification.Cell, doi:10.1016/j.cell.2012.09.016 (2012)), Xiao et al., unpublisheddata). For instance, genes that are down-regulated following knockdownof the ‘Th17-positive’ regulator Mina are highly enriched (p<10⁻⁶) inthe late induced clusters (e.g., C19, C20). Conversely, genes in thesame late induced clusters become even more up-regulated followingknockdown of the ‘Th17 negative’ regulator Sp4.

Mina Promotes the Th17 Program and Inhibits the Foxp3 Program:

Knockdown of Mina, a chromatin regulator from the Jumonji C (JmjC)family, represses the expression of signature Th17 cytokines and TFs(e.g. ROR-γt, Batf, Irf4) and of late-induced genes (clusters C9, C19;p<10⁻⁵), while increasing the expression of Foxp3, the master TF of Tregcells. Mina is strongly induced during Th17 differentiation (clusterC7), is down-regulated in IL23r−/− Th17 cells, and is a predicted targetof Batf (Glasmacher, E. et al. A Genomic Regulatory Element That DirectsAssembly and Function of Immune-Specific AP-1-IRF Complexes. Science,doi:10.1126/science.1228309 (2012)), ROR-γt (Glasmacher et al., Science2012), and Myc in the model (FIG. 5a ). Mina was shown to suppress Th2bias by interacting with the TF NFAT and repressing the IL-4 promoter(Okamoto, M. et al. Mina, an Il4 repressor, controls T helper type 2bias. Nat. Immunol. 10, 872-879, doi:10.1038/ni.1747 (2009)). However,in the cells, Mina knockdown did not induce Th2 genes, suggesting analternative mode of action via positive feedback loops between Mina,Batf and ROR-γt (FIG. 5a , left). Consistent with this model, Minaexpression is reduced in Th17 cells from ROR-γt-knockout mice, and theMina promoter was found to be bound by ROR-γt by ChIP-Seq (data notshown). Finally, the genes induced by Mina knockdown significantlyoverlap with those bound by Foxp3 in Treg cells (Marson et al., Nature2007; Zheng et al., Nature 2007) (P<10⁻²⁵) and with a cluster previouslylinked to Foxp3 activity in Treg cells (Hill, J. A. et al. Foxp3transcription-factor-dependent and -independent regulation of theregulatory T cell transcriptional signature. Immunity 27, 786-800,doi:S1074-7613(07)00492-X [pii]10.1016/j.immuni.2007.09.010 (2007))(FIG. 15c ). When comparing to previously defined transcriptionalsignatures of Treg cells (compared to conventional T cells, (Hill, J. A.et al. Foxp3 transcription-factor-dependent and -independent regulationof the regulatory T cell transcriptional signature. Immunity 27,786-800, doi:10.1016/j.immuni.2007.09.010 (2007))), genes that areinduced in the Mina knockdown are enriched in a cluster tightly linkedto functional activity of FoxP3. Conversely, genes down-regulated in theMina knockdown are more directly responsive to TCR and IL-2 and lessresponsive to Foxp3 in Treg cells (FIG. 15c ).

To further analyze the role of Mina, IL-17a and Foxp3 expression wasmeasured following differentiation of naïve T cells from Mina−/− mice.Mina−/− cells had decreased IL-17a and increased Foxp3 compared towild-type (WT) cells, as detected by intracellular staining (FIG. 5a ).Cytokine analysis of the corresponding supernatants confirmed a decreasein IL-17a production and an increase in IFN-γ (FIG. 5a ) and TNF-α (FIG.16a ). Under Th17 differentiation conditions, loss of Mina resulted in adecrease in IL-17 expression and increase in FoxP3, as detected byintracellular staining (FIG. 5a ). Cytokine analysis of the supernatantsfrom these differentiating cultures confirmed a decrease in IL-17production with a commensurate increase in IFNγ (FIG. 5a ) and TNFα(FIG. 16a ).

The reciprocal relationship between Tregs/Th17 cells has been welldescribed (Korn, T. et al. IL-21 initiates an alternative pathway toinduce proinflammatory T(H)17 cells. Nature 448, 484-487,doi:10.1038/nature05970 (2007)), and it was assumed that this isachieved by direct binding of the ROR-γt/Foxp3 TFs. However, theanalysis suggests a critical role for the regulator Mina in mediatingthis process. This suggests a model where Mina, induced by ROR-γt andBatf, promotes transcription of ROR-γt, while suppressing induction ofFoxp3, thus affecting the reciprocal Tregs/Th17 balance (Korn, et al.,Nature 2007)) by favoring rapid Th17 differentiation.

Fas Promotes the Th17 Program and Suppresses IFN-γ Expression:

Fas, the TNF receptor superfamily member 6, is another Th17 positiveregulator (FIG. 5b ). Fas is induced early, and is a target of Stat3 andBatf in the model. Fas knockdown represses the expression of key Th17genes (e.g., IL-17a, IL-17f, Hif1a, Irf4, and Rbpj) and of the inducedcluster C14, and promotes the expression of Th1-related genes, includingIFN-γ receptor 1 and Klrd1 (Cd94; by RNA-Seq, FIG. 4, FIG. 5b , and FIG.15). Fas and Fas-ligand deficient mice are resistant to the induction ofautoimmune encephalomyelitis (EAE) (Waldner, H., Sobel, R. A., Howard,E. & Kuchroo, V. K. Fas- and FasL-deficient mice are resistant toinduction of autoimmune encephalomyelitis. J Immunol 159, 3100-3103(1997)), but have no defect in IFN-γ or Th1 responses. The mechanismunderlying this phenomenon was never studied.

To explore this, T cells from Fas−/− mice (FIG. 5b , FIG. 16c ) weredifferentiated. Consistent with the knockdown analysis, expression ofIL-17a was strongly repressed and IFN-γ production was stronglyincreased under both Th17 and Th0 polarizing conditions (FIG. 5b ).These results suggest that besides being a death receptor, Fas may playan important role in controlling the Th1/Th17 balance, and Fas−/− micemay be resistant to EAE due to lack of Th17 cells.

Pou2af1 Promotes the Th17 Program and Suppresses IL-2 Expression:

Knockdown of Pou2af1 (OBF1) strongly decreases the expression of Th17signature genes (FIG. 5c ) and of intermediate- and late-induced genes(clusters C19 and C20, p<10⁻⁷), while increasing the expression ofregulators of other CD4+ subsets (e.g., Foxp3, Stat4, Gata3) and ofgenes in non-induced clusters (clusters C2 and C16 p<10⁻⁹). Pou2af1'srole in T cell differentiation has not been explored (Teitell, M. A.OCA-B regulation of B-cell development and function. Trends Immunol 24,546-553 (2003)). To investigate its effects, T cells from Pou2af1−/−mice were differentiated (FIG. 5c , FIG. 16b ). Compared to WT cells,IL-17a production was strongly repressed. Interestingly, IL-2 productionwas strongly increased in Pou2af1−/− T cells under non-polarizing (Th0)conditions. Thus, Pou2af1 may promote Th17 differentiation by blockingproduction of IL-2, a known endogenous repressor of Th17 cells(Laurence, A. et al. Interleukin-2 signaling via STAT5 constrains Thelper 17 cell generation. Immunity 26, 371-381,doi:S1074-7613(07)00176-8 [pii]10.1016/j.immuni.2007.02.009 (2007)).Pou2af1 acts as a transcriptional co-activator of the TFs OCT1 or OCT2(Teitell, Trends Immunol 2003). IL-17a production was also stronglyrepressed in Oct1-deficient cells (FIG. 16d ), suggesting that Pou2af1may exert some of its effects through this co-factor.

TSC22d3 May Limit Th17 Differentiation and Pro-Inflammatory Function:

Knockdown of the TSC22 domain family protein 3 (Tsc22d3) increases theexpression of Th17 cytokines (IL-17a, IL-21) and TFs (ROR-γt, Rbpj,Batf), and reduces Foxp3 expression. Previous studies in macrophageshave shown that Tsc22d3 expression is stimulated by glucocorticoids andIL-10, and it plays a key role in their anti-inflammatory andimmunosuppressive effects (Choi, S.-J. et al. Tsc-22 enhances TGF-betasignaling by associating with Smad4 and induces erythroid celldifferentiation. Mol. Cell. Biochem. 271, 23-28 (2005)). Tsc22d3knockdown in Th17 cells increased the expression of IL-10 and other keygenes that enhance its production (FIG. 5d ). Although IL-10 productionhas been shown (Korn et al., Nature 2007; Peters, A., Lee, Y. & Kuchroo,V. K. The many faces of Th17 cells. Curr. Opin. Immunol. 23, 702-706,doi:10.1016/j.coi.2011.08.007 (2011); Chaudhry, A. et al. Interleukin-10signaling in regulatory T cells is required for suppression of Th17cell-mediated inflammation. Immunity 34, 566-578,doi:10.1016/j.immuni.2011.03.018 (2011)) to render Th17 cells lesspathogenic in autoimmunity, co-production of IL-10 and IL-17a may be theindicated response for clearing certain infections like Staphylococcusaureus at mucosal sites (Zielinski, C. E. et al. Pathogen-induced humanTH17 cells produce IFN-γ or IL-10 and are regulated by IL-1β. Nature484, 514-518, doi:10.1038/nature10957 (2012)). This suggests a modelwhere Tsc22d3 is part of a negative feedback loop for the induction of aTh17 cell subtype that coproduce IL-17 and IL-10 and limits theirpro-inflammatory capacity. Tsc22d3 is induced in other cells in responseto the steroid Dexamethasone (Jing, Y. et al. A mechanistic study on theeffect of dexamethasone in moderating cell death in Chinese HamsterOvary cell cultures. Biotechnol Prog 28, 490-496, doi:10.1002/btpr.747(2012)), which represses Th17 differentiation and ROR-γt expression (Hu,M., Luo, Y. L., Lai, W. Y. & Chen, P. F. [Effects of dexamethasone onintracellular expression of Th17 cytokine interleukin 17 in asthmaticmice]. Nan Fang Yi Ke Da Xue Xue Bao 29, 1185-1188 (2009)). Thus,Tsc22d3 may mediate this effect of steroids.

To further characterize Tsc22d3's role, ChIP-Seq was used to measure itsDNA-binding profile in Th17 cells and RNA-Seq following its knockdown tomeasure its functional effects. There is a significant overlap betweenTsc22d3's functional and physical targets (P<0.01, e.g., IL-21, Irf4;see Methods in Example 1). For example, Tsc22d3 binds in proximity toIL-21 and Irf4, which also become up regulated in the Tsc22d3 knockdown.Furthermore, the Tsc22d3 binding sites significantly overlap those ofmajor Th17 factors, including Batf, Stat3, Irf4, and ROR-γt (>5 foldenrichment; FIG. 5d , and see Methods in Example 1). This suggests amodel where Tsc22d3 exerts its Th17-negative function as atranscriptional repressor that competes with Th17 positive regulatorsover binding sites, analogous to previous findings in CD4+ regulation(Ciofani et al., Cell 2012; Yang, X. P. et al. Opposing regulation ofthe locus encoding IL-17 through direct, reciprocal actions of STAT3 andSTAT5. Nat. Immunol. 12, 247-254, doi:10.1038/ni.1995 (2011)).

Example 6: Protein C Receptor (PROCR) Regulates Pathogenic Phenotype ofTh17 Cells

Th17 cells, a recently identified T cell subset, have been implicated indriving inflammatory autoimmune responses as well as mediatingprotective responses against certain extracellular pathogens. Based onfactors such as molecular signature, Th17 cells are classified aspathogenic or non-pathogenic. (See e.g., Lee et al., “Induction andmolecular signature of pathogenic Th17 cells,” Nature Immunology, vol.13(10): 991-999 and online methods).

It should be noted that the terms “pathogenic” or “non-pathogenic” asused herein are not to be construed as implying that one Th17 cellphenotype is more desirable than the other. As will be described herein,there are instances in which inhibiting the induction of pathogenic Th17cells or modulating the Th17 phenotype towards the non-pathogenic Th17phenotype or towards another T cell phenotype is desirable. Likewise,there are instances where inhibiting the induction of non-pathogenicTh17 cells or modulating the Th17 phenotype towards the pathogenic Th17phenotype or towards another T cell phenotype is desirable. For example,pathogenic Th17 cells are believed to be involved in immune responsessuch as autoimmunity and/or inflammation. Thus, inhibition of pathogenicTh17 cell differentiation or otherwise decreasing the balance of Th17 Tcells towards non-pathogenic Th17 cells or towards another T cellphenotype is desirable in therapeutic strategies for treating orotherwise ameliorating a symptom of an immune-related disorder such asan autoimmune disease or an inflammatory disorder. In another example,depending on the infection, non-pathogenic or pathogenic Th17 cells arebelieved to be desirable in building a protective immune response ininfectious diseases and other pathogen-based disorders. Thus, inhibitionof non-pathogenic Th17 cell differentiation or otherwise decreasing thebalance of Th17 T cells towards pathogenic Th17 cells or towards anotherT cell phenotype or vice versa is desirable in therapeutic strategiesfor treating or otherwise ameliorating a symptom of an immune-relateddisorder such as infectious disease.

Th17 cells are considered to be pathogenic when they exhibit a distinctpathogenic signature where one or more of the following genes orproducts of these genes is upregulated in TGF-β3-induced Th17 cells ascompared to TGF-β1-induced Th17 cells: Cxcl3, Il22, Il3, Ccl4, Gzmb,Lrmp, Ccl5, Casp1, Csf2, Ccl3, Tbx21, Icos, Il7r, Stat4, Lgals3 or Lag3.Th17 cells are considered to be non-pathogenic when they exhibit adistinct non-pathogenic signature where one or more of the followinggenes or products of these genes is down-regulated in TGF-β3-inducedTh17 cells as compared to TGF-β1-induced Th17 cells: Il6st, Il1rn,lkzf3, Maf, Ahr, Il9 or Il10.

A temporal microarray analysis of developing Th17 cells was performed toidentify cell surface molecules, which are differentially expressed inTh17 cells and regulate the development of Th17 cells. PROCR wasidentified as a receptor that is differentially expressed in Th17 cellsand found its expression to be regulated by Th17-specific transcriptionregulators.

Protein C receptor (PROCR; also called EPCR or CD201) is primarilyexpressed on endothelial cells, CD8⁺ dendritic cells and was alsoreported to be expressed to lower levels on other hematopoietic andstromal cells. It binds to activated protein C as well as factorVII/VIIa and factor Xa and was shown to have diverse biologicalfunctions, including anticoagulant, cytoprotective, anti-apoptotic andanti-inflammatory activity. However, prior to these studies, thefunction of PROCR in T cells had not been explored.

The biological function of PROCR and its ligand activated protein C inTh17 cells was analyzed, and it was found that it decreased theexpression of some of the genes identified as a part of the pathogenicsignature of Th17 cells. Furthermore, PROCR expression in Th17 cellsreduced the pathogenicity of Th17 cells and ameliorated disease in amouse model for human multiple sclerosis.

These results imply that PROCR functions as a regulatory gene for thepathogenicity of Th17 cells through the binding of its ligand(s). It istherefore conceivable that the regulation of this pathway might beexploited for therapeutic approaches to inflammatory and autoimmunediseases.

These studies are the first to describe the Th17-specific expression ofPROCR and its role in reducing autoimmune Th17 pathogenicity. Thus,activation of PROCR through antibodies or other agonists are useful as atherapeutic strategy in an immune response such as inflammatoryautoimmune disorders. In addition, blocking of PROCR through antibodiesor other inhibitors could be exploited to augment protective Th17responses against certain infectious agents and pathogens.

PROCR is Expressed in Th17 Cells:

The membrane receptor PROCR (Protein C receptor; also called EPCR orCD201) is present on epithelial cells, monocytes, macrophages,neutrophils, eosinophils, and natural killer cells but its expressionhad not previously been reported on T cells (Griffin J H, Zlokovic B V,Mosnier L O. 2012. Protein C anticoagulant and cytoprotective pathways.Int J Hematol 95: 333-45). However, the detailed transcriptomic analysisof Th17 cells described herein has identified PROCR as an important nodefor Th17 cell differentiation (Yosef N, Shalek A K, Gaublomme J T, JinH, Lee Y, Awasthi A, Wu C, Karwacz K, Xiao S, Jorgolli M, Gennert D,Satija R, Shakya A, Lu D Y, Trombetta J J, Pillai M R, Ratcliffe P J,Coleman M L, Bix M, Tantin D, Park H, Kuchroo V K, Regev A. 2013.Dynamic regulatory network controlling TH17 cell differentiation. Nature496: 461-8). PROCR shares structural homologies with the CD1/MHCmolecules and binds activated protein C (aPC) as well as bloodcoagulation factor VII and the Vγ4Vδ5 TCR of γδ T cells. Due to itsshort cytoplasmic tail PROCR does not signal directly, but rathersignals by associating with the G-protein-coupled receptor PAR1 (FIG.30a ; (Griffin et al, Int J Hematol 95: 333-45 (2012))). To analyzePROCR expression on Th subsets, CD4+ T cells were differentiated invitro under polarizing conditions and determined PROCR expression. Asindicated by the network analysis of Th17 cells, high levels of PROCRcould be detected in cells differentiated under Th17 conditions (FIG.31b ). To study expression of PROCR on Th17 cells during an immuneresponse, mice were immunized with MOG/CFA to induce EAE. PROCR was notexpressed on T cells in spleen and lymph nodes. In contrast, it could bedetected on Th17 cells infiltrating the CNS (FIG. 31c ). These dataindicate that PROCR is expressed on Th17 cells in vitro and in vivo,where it is largely restricted to T cells infiltrating the target organ.To investigate the functions of PROCR in Th17 cells, studies weredesigned to test how loss of PROCR would affect IL-17 production using Tcells from a PROCR hypomorphic mutant (PROCRd/d). PROCR deficiencycauses early embryonic lethality (embryonic day 10.5) (Gu J M, Crawley JT, Ferrell G, Zhang F, Li W, Esmon N L, Esmon C T. 2002. Disruption ofthe endothelial cell protein C receptor gene in mice causes placentalthrombosis and early embryonic lethality. J Biol Chem 277: 43335-43),whereas hypomorphic expression of PROCR, which retain only small amounts(<10% of wild-type) of PROCR, is sufficient to completely abolishlethality and mice develop normally under steady state conditions(Castellino F J, Liang Z, Volkir S P, Haalboom E, Martin J A,Sandoval-Cooper M J, Rosen E D. 2002. Mice with a severe deficiency ofthe endothelial protein C receptor gene develop, survive, and reproducenormally, and do not present with enhanced arterial thrombosis afterchallenge. Thromb Haemost 88: 462-72). When challenged in a model forseptic shock, PROCRd/d mice show compromised survival compared to WTmice (Iwaki T, Cruz D T, Martin J A, Castellino F J. 2005. Acardioprotective role for the endothelial protein C receptor inlipopolysaccharide-induced endotoxemia in the mouse. Blood 105:2364-71). Naïve CD4+ PROCRd/d T cells differentiated under Th17conditions produced less IL-17 compared to WT naïve CD4+ T cells (FIG.31d ). Effector memory PROCRd/d T cells cultured with IL-23 producedmore IL-17 than WT memory T cells. Therefore PROCR, similar to PD-1,promotes generation of Th17 cells from naïve CD4 T cells, but inhibitsthe function of Th17 effector T cells.

Knockdown Analysis of PROCR in Tumor Model:

FIG. 44 is a graph depicting B16 tumor inoculation of PROCR mutant mice.7 week old wild type or PROCR mutant (EPCR delta) C57BL/6 mice wereinoculated with 5×10⁵ B16F10 melanoma cells. As shown in FIG. 44,inhibition of PROCR slowed tumor growth. Thus, inhibition of PROCR isuseful for impeding tumor growth and in other therapeutic applicationsfor treatment of cancer.

PD-1 and PROCR Affect Th17 Pathogenicity:

Th17 cells are very heterogeneous and the pathogenicity of Th17 subsetsdiffers depending on the cytokine environment during theirdifferentiation (Zielinski C E, Mele F, Aschenbrenner D, Jarrossay D,Ronchi F, Gattorno M, Monticelli S, Lanzavecchia A, Sallusto F. 2012.Pathogen-induced human TH17 cells produce IFN-gamma or IL-10 and areregulated by IL-1beta. Nature 484: 514-8; Lee Y, Awasthi A, Yosef N,Quintana F J, Peters A, Xiao S, Kleinewietfeld M, Kunder S, Sobel R A,Regev A, Kuchroo V. 2012. Induction and molecular signature ofpathogenic Th17 cells. Nat Immunol In press; and Ghoreschi K, LaurenceA, Yang X P, Tato C M, McGeachy M J, Konkel J E, Ramos H L, Wei L,Davidson T S, Bouladoux N, Grainger J R, Chen Q, Kanno Y, Watford W T,Sun H W, Eberl G, Shevach E M, Belkaid Y, Cua D J, Chen W, O'Shea J J.2010. Generation of pathogenic T(H)17 cells in the absence of TGF-betasignalling. Nature 467: 967-71). In addition to the cytokine milieu,several costimulatory pathways have been implicated in regulatingdifferentiation and function of T helper subsets, including Th17 cells.CTLA-4-B7 interactions inhibit Th17 differentiation (Ying H, Yang L,Qiao G, Li Z, Zhang L, Yin F, Xie D, Zhang J. 2010. Cutting edge:CTLA-4-B7 interaction suppresses Th17 cell differentiation. J Immunol185: 1375-8). Furthermore, the work described herein revealed that ICOSplays a critical role in the maintenance of Th17 cells (Bauquet A T, JinH, Paterson A M, Mitsdoerffer M, Ho I C, Sharpe A H, Kuchroo V K. 2009.The costimulatory molecule ICOS regulates the expression of c-Maf andIL-21 in the development of follicular T helper cells and TH-17 cells.Nat Immunol 10: 167-75).

Based on the detailed genomic analysis of pathogenic vs. non-pathogenicTh17 cells herein, it has been determined that the molecular signaturesthat define pathogenic vs. non-pathogenic effector Th17 cells inautoimmune disease (Lee Y, Awasthi A, Yosef N, Quintana F J, Peters A,Xiao S, Kleinewietfeld M, Kunder S, Sobel R A, Regev A, Kuchroo V. 2012.Induction and molecular signature of pathogenic Th17 cells. Nat ImmunolIn press). Interestingly, PROCR is part of the signature fornon-pathogenic Th17 cells and its expression is highly increased innon-pathogenic subsets (FIG. 32a ). Furthermore, PROCR seems to play afunctional role in regulating Th17 pathogenicity as engagement of PROCRby its ligand aPC induces some non-pathogenic signature genes, whileTh17 cells from PROCRd/d mice show decreased expression of these genes(FIG. 32b ). To study whether PROCR could also affect pathogenicity ofTh17 cells in an in vivo model of autoimmunity, an adoptive transfermodel for EAE was used. To induce disease, MOG-specific 2D2 TCRtransgenic T cells were differentiated under Th17 conditions and thentransferred into naïve recipients. As shown in FIG. 32c , forcedoverexpression of PROCR on Th17 cells ameliorated disease, confirmingthat PROCR drives conversion of pathogenic towards non-pathogenic Th17cells. In addition, it was found that PD-1:PD-L1 interactions limit thepathogenicity of effector Th17 cells in vivo. When MOG35-55-specific(2D2) Th17 effector cells were transferred into WT vs. PD-L1−/− mice,PD-L1−/− recipients rapidly developed signs of EAE (as early as day 5post transfer), and EAE severity was markedly increased with mostexperiments needed to be terminated due to rapid onset of morbidity inPD-L1−/− recipients (FIG. 32d ). The number of CNS-infiltrating cellswas significantly increased in PD-L1−/− recipients with a greaterpercentage of 2D2+ IL-17+ in PD-L1−/− recipients compared to WT mice.Therefore both PD-1 and PROCR seem to control pathogenicity of effectorTh17 cells.

Several co-inhibitory molecules have been implicated in T celldysfunction during antigen persistence. PD-1 and Tim-3, in particular,have wide implications in cancer and chronic viral infections such asHIV, HCV in human and LCMV in mice. Autoreactive T cell responses inmice and human are characterized with reduced expression of inhibitorymolecules. The ability to induce T cell dysfunction in autoimmunesettings could be clinically beneficial. MS patients that respond toCopaxone treatment show significantly elevated levels of expression ofPROCR and PD-L1. It has been previously demonstrated that increasingTim-3 expression and promoting T cell exhaustion provides the ability tolimit encephalitogenecity of T cells and reduce EAE severity (RangachariM, Zhu C, Sakuishi K, Xiao S, Karman J, Chen A, Angin M, Wakeham A,Greenfield E A, Sobel R A, Okada H, McKinnon P J, Mak T W, Addo M M,Anderson A C, Kuchroo V K. 2012. Bat3 promotes T cell responses andautoimmunity by repressing Tim-3-mediated cell death and exhaustion. NatMed 18: 1394-400). Studies were, therefore, designed to determinewhether the novel inhibitory molecule PROCR, which is selectivelyenriched in Th17 cells, could also play a role in T cell exhaustion. Itwas found that PROCR is expressed in exhausted tumor infiltratinglymphocytes that express both PD-1 and Tim-3 (FIG. 33a ). Consistentwith this observation, it was found that PROCR was most enriched inantigen-specific exhausted CD8 T cells (FIG. 33b ) during chronic LCMVinfection. While T cell exhaustion is detrimental in chronic viralinfection and tumor immunity, induction of exhaustion may play abeneficial role in controlling potentially pathogenic effector cellsthat cause autoimmune diseases. Regulating the expression and/orfunction of PD-1 and PROCR might provide the avenues to accomplish thistask in controlling autoimmunity.

Example 7: Fas in Th Cell Differentiation

Fas, also known as FasR, CD95, APO-1, TNFRSF6, is a member of the TNFreceptor superfamily. Binding of FasL leads to FAS trimers that bindFADD (death domains), which activates caspase-8 and leads to apoptosis.Fas also exhibits apoptosis independent effects such as interaction withAkt, STAT3, and NF-κB in liver cells and interaction with NF-κB and MAPKpathways in cancer cells.

Lpr mice are dominant negative for Fas (transposon intron 1), creating afunctional knockout (KO). These mice exhibit lymphoproliferative disease(lpr); age dependent >25-fold size increase of LN, Spleen; expansion ofThy1+B220+CD4−CD8−TCRa/b+ T cells. These mice produce spontaneousanti-dsDNA Ab, systemic autoimmunity, which makes them a model ofsystemic lupus erythematosus (SLE), but these mice are resistant toexperimental autoimmune encephalomyelitis (EAE). Gld mice are dominantnegative for FasL. Fas flox mice that areCD4Cre-/CD19Cre-/CD4Cre-CD19Cre-/LckCre-Fasflox exhibit nolymphoproliferation and no expansion of Thy1+B220+CD4−CD8−TCRa/b+ Tcells. These mice do exhibit progressive lymphopenia, inflammatory lungfibrosis, and wasting syndrome. Fas flox mice that areMxCre+poly(IC)-Fasflox exhibit an 1pr phenotype. Fas flox mice that areMOGCre-Fasflox are resistant to EAE. Fas flox mice that areLysMCre-Fasflox exhibit lymphoproliferation and glomerulonephritis.

Although Fas (CD95) has been identified as a receptor mediatingapoptosis, the data herein clearly show that Fas is important for Th17differentiation and development of EAE. The data herein demonstratesthat Fas-deficient mice have a defect in Th17 cell differentiation andpreferentially differentiate into Th1 and Treg cells. The expansion ofTreg cells and inhibition of Th17 cells in Fas-deficient mice might beresponsible for disease resistance in EAE.

Fas-deficient cells are impaired in their ability to differentiate intoTh17 cells, and they produce significantly lower levels of IL-17 whencultured in vitro under Th17 conditions (IL-1β+IL-6+IL-23). Furthermore,they display reduced levels of IL-23R, which is crucial for Th17 cellsas IL-23 is required for Th17 stability and pathogenicity. In contrast,Fas inhibits IFN-γproduction and Th1 differentiation, as cells derivedfrom Fas-deficient mice secrete significantly higher levels of IFN-γ.Similarly, Fas-deficient cells more readily differentiate into Foxp3+Tregs and secrete higher levels of the Treg effector cytokine IL-10. Ittherefore seems as if Fas suppresses the differentiation into Tregs andIFN-γ-producing Th1 cells while promoting Th17 differentiation. Ininflammatory autoimmune disorders, such as EAE, Fas therefore seems topromote disease progression by shifting the balance in T helper cellsaway from the protective Tregs and from IFN-γ-producing Th1 cellstowards pathogenic Th17 cells.

Example 8: Targeting CD5L in Modulation of Th17 Pathogenic State

CD5L (CD5 antigen-like; AIM (apoptosis inhibitor of macrophage)) hasbeen identified as a novel regulator of the Th17 pathogenic state. CD5Lis a 54-kD protein belongs to the macrophage scavenger receptorcysteine-rich domain superfamily; other family member include CD5, CD6,CD36, MARCO etc. CD5L is expressed by macrophage and adipocytes and isincorporated into cells through CD36 (Kurokawa J. et al. 2010). CD5L canbe induced by activation of RXR/LXR (Valledor A F et al. 2004) andinhibits lipid induced apoptosis of thymocytes and macrophage. CD5L isinvolved in obesity-associated autoantibody production (Arai S et. al.2013) and plays a role in lipid metabolism. CD5L promotes lipolysis inadipocytes, potentially preventing obesity onset (Miyazaki T et al.2011) and inhibits de novo lipid synthesis by inhibiting fatty acidsynthase (Kurokawa J. et al. 2010).

FIGS. 34A-34C are a series of graphs demonstrating the expression ofCD5L on Th17 cells. FIGS. 35A-35C are a series of illustrations andgraphs depicting how CD5L deficiency does not alter Th17differentiation. FIGS. 36A-36C are a series of illustrations and graphsdepicting how CD5L deficiency alters Th17 memory by affecting survivalor stability.

FIGS. 37A-37B are a series of graphs depicting how CD5L deficiencyresults in more severe and prolonged EAE with higher Th17 responses.FIGS. 38A-38C are a series of illustrations and graphs depicting howloss of CD5L converts non-pathogenic Th17 cells into pathogenic effectorTh17 cells. FIGS. 39A-39B are a series of graphs depicting howCD5L-deficient Th17 cells (TGF-β+IL-6) develop a pathogenic phenotype.

Example 9: Single Cell Analysis of Functional Th17 Data

Single cell analysis of target genes that can be exploited fortherapeutic and/or diagnostic uses allows for the identification ofgenes that either cannot be identified at a population level or are nototherwise ready apparent as a suitable target gene at the populationlevel.

Single-cell RNA sequencing provides a unique opportunity to characterizedifferent sub-types of Th17 cells and to gain better understanding ofthe regulatory mechanisms that underlie their heterogeneity andplasticity. In particular, the studies described herein were designed toidentify subpopulations of Th17 cells both in-vitro and in-vivo, and tomap the potential divergent mechanisms at play. These results provideimportant mechanistic insights with the potential for therapeuticrelevance in treatment of autoimmune-disease.

Using a microfluidic technology (Fluidigm C1) for preparation ofsingle-cell mRNA SMART-Seq libraries, differentiated Th17 cells (96cells at a time) were profiled in-vitro under pathogenic andnon-pathogenic polarizing conditions at two time points (48h and 96hinto the differentiation process). In addition, Th17 cells isolated fromthe central nervous system and lymph nodes were profiled at the peak ofdisease of mice immunized with experimental autoimmune encephalomyelitis(EAE; a mouse model of multiple sclerosis). A computational pipeline wasthen developed for processing and analyzing the resulting data set(˜1000 cells altogether). The results offer a vantage point into thesources and functional implications of expression patterns observed atthe single cell level, expression modality, i.e., map how a gene isexpressed across the population, and variability, i.e., how tightly theexpression level of a gene is regulated.

For instance, it was found that the signature cytokine IL-17A exhibitsone of the highest levels of variability in the cell's transcriptomein-vitro. This variation strongly correlates with an unsupervisedpartition of the cells into sub-populations, which spans the spectrumbetween potentially pathogenic cells (high levels of IL-17A and lowlevels of immunosuppressive cytokines like IL-10) to non-pathogeniccells (opposite expression profiles).

The specific genes that characterize the two extreme states provideappealing target genes and include candidates that were not detected byprevious, population-level approaches (Yosef, N. et al. Dynamicregulatory network controlling TH17 cell differentiation. Nature 496,461-468, doi:10.1038/nature11981 (2013)). To identify the most promisingtarget genes, a gene prioritization scheme, which combines the singlecell RNA-seq results with multiple other sources of information (e.g.,transcription factor binding), was developed. High-ranking targets werethen further analyzed using the respective knockout mice.

The following provides single cell analysis methods and conditions toinduce various T cell phenotypes:

-   -   Condition Th0: cells are activated with CD3/CD28, but no        cytokines are added to the media as a control    -   Condition T16: CD3/CD28 activation+TGFβ1+IL6 are added to media        to produce non-pathogenic Th17 conditions    -   Condition T36: CD3/CD28 activation+TGFβ3+IL6 are added to media        to produce pathogenic Th17 conditions    -   Condition B623: CD3/CD28 activation+IL1β+IL6+IL23 are added to        media to produce pathogenic Th17 conditions    -   Condition T: CD3/CD28 activation+TGFβ1 are added to media to        produce Treg conditions

Under condition Th0, proliferation of cell is activated but the cellsare not influenced toward a specific outcome. Under conditions T16, T36and B623, the activated, proliferating cells are influenced toward aspecific Th17 cell outcome, as indicated above. Again, the terms“pathogenic” or “non-pathogenic” as used herein are not to be construedas implying that one Th17 cell phenotype is more desirable than theother. They are being used to connote different Th17 cell phenotypeswith different identifying characteristics.

The following methods were used in the studies described herein: Mice:C57BL/6 wild-type, CD4−/−(2663). Mice were obtained from JacksonLaboratory. IL-17A-GFP mice were from Biocytogen. In addition, spleensand lymph nodes from GPR65−/− mice were provided by Yang Li. ZBTB32−/−mice were obtained from the laboratory of Pier Paolo Pandolfi. Cellsorting and in vitro T-cell differentiation: CD4+ T cells were purifiedfrom spleen and lymph nodes using anti-CD4 microbeads (Miltenyi Biotech)then stained in PBS with 1% FCS for 20 min at room temperature withanti-CD4-PerCP, anti-CD62l-APC and anti-CD44-PE antibodies (allBiolegend). Naïve CD4+CD62l highCD44low T cells were sorted using the BDFACSAria cell sorter. Sorted cells were activated with plate-boundanti-CD3 (2 μg ml-1) and anti-CD28 (2 μg ml-1) in the presence ofcytokines. For TH17 differentiation, the following reagents were used: 2ng/ml recombinant human TGF-β1 and recombinant human TGF-β3 (MiltenyiBiotec), 25 ng/ml recombinant mouse IL-6 (Miltenyi Biotec), 20 ng/mlrecombinant mouse IL-23 (R&D Biosystems) and 20 ng/ml recombinant mouseIL-1β (Miltenyi Biotec). Cells were cultured for 48-96 h and collectedfor RNA, intracellular cytokine staining, flow-fish.

CyTOF and flow cytometry: Active induction of EAE and disease analysis:For active induction of EAE, mice were immunized by subcutaneousinjection of 100 μg MOG (35-55) (MEVGWYRSPFSRVVHLYRNGK) (SEQ ID NO:1395) in CFA, then received 200 ng pertussis toxin intraperitoneally(List Biological Laboratory) on days 0 and 2. Mice were monitored andwere assigned scores daily for development of classical and atypicalsigns of EAE according to the following criteria: 0, no disease; 1,decreased tail tone or mild balance defects; 2, hind limb weakness,partial paralysis or severe balance defects that cause spontaneousfalling over; 3, complete hind limb paralysis or very severe balancedefects that prevent walking; 4, front and hind limb paralysis orinability to move body weight into a different position; 5, moribundstate (ger, A., Dardalhon, V., Sobel, R. A., Bettelli, E. & Kuchroo, V.K. Th1, Th17, and Th9 effector cells induce experimental autoimmuneencephalomyelitis with different pathological phenotypes. Journal ofimmunology 183, 7169-7177, doi:10.4049/jimmunol.0901906 (2009)).

Isolation of T-cells from EAE mice at the peak of disease: At the peakof disease, mice T-cells were collected from the draining lymph nodesand the CNS. For isolation from the CNS, mice were perfused through theleft ventricle of the heart with cold PBS. The brain and the spinal cordwere flushed out with PBS by hydrostatic pressure. CNS tissue was mincedwith a sharp razor blade and digested for 20 min at 37° C. withcollagenase D (2.5 mg/ml; Roche Diagnostics) and DNaseI (1 mg/ml;Sigma). Mononuclear cells were isolated by passage of the tissue througha cell strainer (70 μm), followed by centrifugation through a Percollgradient (37% and 70%). After removal of mononuclear cells, thelymphocytes were washed, stained and sorted for CD3 (Biolegend), CD4(Biolegend), 7AAD and IL17a-GFP or FOXP3-GFP.

Whole transcriptome amplification: Cell lysis and SMART-Seq (amskold, D.et al. Full-length mRNA-Seq from single-cell levels of RNA andindividual circulating tumor cells. Nature Biotechnology 30, 777-782(2012)) whole transcriptome amplification (WTA) was performed on the C1chip using the C1 Single-Cell Auto Prep System (C1 System) using theSMARTer Ultra Low RNA Kit for Illumina Sequencing (Clontech) with thefollowing modifications:

Cell Lysis Mix:

Composition Stock Conc. Volume C1 Loading Reagent 20X 0.60 ul SMARTerKit RNase Inhibitor 40 x 0.30 ul SMARTer Kit 3′ SMART CDS Primer II A 12μM 4.20 ul SMARTer Kit Dilution Buffer  1X 6.90 ul

Cycling Conditions I:

a) 72° C., 3 min

b) 4° C., 10 min

c) 25° C., 1 min

Reverse Transcription (RT) Reaction Mix:

Composition Stock Conc. Volume C1 Loading Reagent 20.0 x 0.45 ul SMARTerKit 5X First-Strand Buffer 5.0 x 4.20 ul (RNase-Free) SMARTer KitDithiothreitol 100 mM 0.53 ul SMARTer Kit dNTP Mix (dATP, dCTP, dGTP, 10mM 2.10 ul and dTTP, each at 10 mM) SMARTer Kit SMARTer II AOligonucleotide 12 uM 2.10 ul SMARTer Kit RNase Inhibitor 40 x 0.53 ulSMARTer Kit SMARTScribe ™ Reverse 100.0 x 2.10 ul Transcriptase

Cycling Conditions II:

a) 42° C., 90 min

b) 70° C., 10 min

PCR Mix:

Composition Stock Conc. Volume PCR Water — 35.2 ul  10X Advantage 2 PCRBuffer 10.0 x 5.6 ul 50X dNTP Mix 10 mM 2.2 ul IS PCR primer 12 uM 2.2ul 50X Advantage 2 Polymerase Mix 50.0 x 2.2 ul C1 Loading Reagent 20.0x 2.5 ul

Cycling Conditions III:

a) 95° C., 1 min

b) 5 cycles of:

i) 95° C., 20s

ii) 58° C., 4 min

ii) 68° C., 6 min

c) 9 cycles of:

i) 95° C., 20s

ii) 64° C., 30s

ii) 68° C., 6 min

d) 7 cycles of:

i) 95° C., 30s

ii) 64° C., 30s

ii) 68° C., 7 min

e) 72° C., 10 min

Library preparation and RNA-Seq: WTA products were harvested from the C1chip and cDNA libraries were prepared using Nextera XT DNA Samplepreparation reagents (Illumina) as per the manufacturer'srecommendations, with minor modifications. Specifically, reactions wererun at 1/4 the recommended volume, the tagmentation step was extended to10 minutes, and the extension time during the PCR step was increasedfrom 30s to 60s. After the PCR step, all 96 samples were pooled withoutlibrary normalization, cleaned twice with 0.9× AMPure XP SPRI beads(Beckman Coulter), and eluted in buffer TE. The pooled libraries werequantified using Quant-IT DNA High-Sensitivity Assay Kit (Invitrogen)and examined using a high sensitivity DNA chip (Agilent). Finally,samples were sequenced deeply using either a HiSeq 2000 or a HiSeq 2500sequencer.

RNA-Seq of population controls: Population controls were generated byextracting total RNA using RNeasy plus Micro RNA kit (Qiagen) accordingto the manufacturer's recommendations. Subsequently, 1 μL of RNA inwater was added to 2 μL of lysis reaction mix, thermocycled usingcycling conditions I (as above). Next, 4 μL of the RT Reaction Mix wereadded and the mixture was thermocycled using cycling conditions II (asabove). Finally, 1 μL of the total RT reaction was added to 9 μL of PCRmix and that mixture was thermocycled using cycling conditions III (asabove). Products were quantified, diluted to 0.125 ng/μL and librarieswere prepared, cleaned, and tested as above.

Flow cytometry and intracellular cytokine staining: Sorted naïve T cellswere stimulated with phorbol 12-myristate 13-aceate (PMA) (50 ng/ml,Sigma-aldrich), ionomycin (1 μg/ml, Sigma-aldrich) and a proteintransport inhibitor containing monensin (Golgistop) (BD Biosciences) for4 h before detection by staining with antibodies. Surface markers werestained in PBS with 1% FCS for 20 min at room temperature, thensubsequently the cells were fixed in Cytoperm/Cytofix (BD Biosciences),permeabilized with Perm/Wash Buffer (BD Biosciences) and stained withBiolegend conjugated antibodies, that is, Brilliant violet 650anti-mouse IFN-γ (XMG1.2) and allophycocyanin-anti-IL-17A(TC11-18H10.1), diluted in Perm/Wash buffer as described14. Foxp3staining was performed with the Foxp3 staining kit by eBioscience(00-5523-00) in accordance with their ‘One-step protocol forintracellular (nuclear) proteins’. Data were collected using either aFACS Calibur or LSR II (Both BD Biosciences), then analyzed using FlowJo software (Treestar).

Quantification of cytokine secretion using ELISA: Naïve T cells fromknockout mice and their wild-type controls were cultured as describedabove, their supernatants were collected after 48h and 96h, and cytokineconcentrations were determined by ELISA (antibodies for IL-17 and IL-10from BD Bioscience) or by cytometric bead array for the indicatedcytokines (BD Bioscience), according to the manufacturers' instructions.

RNA-FlowFish analysis of RNA-expression: Cells prepared under the sameconditions as the RNA-seq samples were prepared with the QuantiGene®ViewRNA ISH Cell Assay kit from Affymetrix following the manufacturersprotocol. High throughput image acquisition at 60× magnification with anImageStream X MkII allows for analysis of high-resolution images,including brightfield, of single cells. Genes of interest were targetedby type 1 probes, housekeeping genes by type 4 probes, and nuclei werestained with dapi. Single cells were selected based on cell propertieslike area, aspect ratio (brightfield images) and nuclear staining. As anegative control, the Bacterial DapB gene (Type 1 probe) was used. Spotcounting was performed with the amnis IDEAS software to obtain theexpression distributions.

CyTOF analysis of protein-expression: In-vitro differentiated cells werecultured and harvested at 72h, followed by a 3h stimulation similar tothe flow cytometry protocol described above. Subsequently samples wereprepared as described previously15. In-vivo cells isolated from lymphnodes and CNS from reporter mice were, due to their limited numbers,imbedded in a pool of CD3+ T-cells isolated from a CD4−/− mouse, toallow for proper sample preparation. The cells from the CD4−/− mousewere stained and sorted for CD3+CD4-7AAD-cells to insure that lowamounts of CD4+ staining during CyTOF staining would be obtained, andCD4+ cells from LN and CNS could be identified in silico.

RNA-Seq Profiling of Single Cells During Th17 Differentiation:

The mRNA levels of CD4+naïve T cells differentiated in vitro wereprofiled under two types of polarizing conditions: Tgfβ1+IL6 andTgfβ3+IL6. While both treatments lead to IL17-production (Ghoreschi, K.,Laurence, A., Yang, X. P., Hirahara, K. & O'Shea, J. J. T helper 17 cellheterogeneity and pathogenicity in autoimmune disease. Trends Immunol32, 395-401 (2011)), only the latter results in autoimmunity uponadoptive transfer (ostins, L. et al. Host-microbe interactions haveshaped the genetic architecture of inflammatory bowel disease. Nature491, 119-124 (2012)). Microfluidic chips (Fluidigm C1) were used for thepreparation of single-cell mRNA SMART-Seq libraries. Each polarizingcondition was sampled at 48 hr and 96 hr into the differentiationprocess. In addition to these single cell RNA-seq libraries, theircorresponding bulk populations of at least 10,000 cells, with at leasttwo replicates for each condition and at an average depth of 15 millionreads, were also sequenced.

RNA-seq reads were aligned to the NCBI Build 37 (UCSC mm9) of the mousegenome using Top-Hat. The resulting alignments were processed byCufflinks to evaluate the expression of transcripts. An alternativepipeline based on the RSEM (RNA-Seq by expectation maximization)software for mRNA quantification was also employed. Unless statedotherwise, the results obtained with this alternative pipeline weresimilar to the ones presented herein.

Library quality metrics, such as genomic alignment rates, ribosomal RNAcontamination, and 3′ or 5′ coverage bias, were computed for eachlibrary. Cells that had low values of these parameters were filtered;the remaining cells (˜80% of the total profiled cells) had similarquality metrics. As an additional preprocessing step, principalcomponents that significantly (p<1e-3) correlated with library qualitymetrics were subtracted. Finally, unless stated otherwise, genes fromeach sample that were not appreciably expressed (fragments per kilobaseof exon per million (FPKM)>10) in at least 20% of the sample's cellswere discarded, retaining on average ˜6k genes for the in vitro samplesand ˜3k genes for the in vivo samples.

Although the gene expression levels of population replicates weretightly correlated with one another (Pearson r>0.97, log-scale), therewere substantial differences in expression between individual cells(0.72<r<0.82, mean: 0.78; FIG. 1b ). Despite this extensive cell-to-cellvariation, the average expression across all single cells correlatedwell with the population data (r>0.92).

Single Cell Profiles Reveal IL17-Related Heterogeneity In Vitro:

Considering the distribution of the expression from individual genesacross cells differentiated with Tgfβ1+IL6, a wide spectrum of behaviorswas observed. About 40% of the analyzed genes were constitutivelyexpressed in all cells. Reassuringly, this set of genes is highlyenriched for housekeeping genes (p<x). However, constitutive expressionof TH17 signature cytokines (for example, IL17f, IL9 and IL21) andearly-acting transcription factors (e.g. Rorc, Irf4, Batf, Stat3, Hif1a,and Mina) was also seen. The remaining genes exhibit a bimodalexpression patterns with high mRNA levels in at least 20% of the cellsand a much lower (often undetectable) levels in the remaining cells.Interestingly, the bimodal genes include key TH17 signature cytokines,chemokines and their receptors (for example, IL23r, IL17a, Ccl20).Bimodatlity was also seen for regulatory cytokines from the IL-10 family(IL10, IL24, IL27ra), as previously observed in population-level data.Finally, a small representation (usually <30% of cells) was seen fortranscription factors and cytokines that characterize other T-celllineages (for example, IL12rb2, Stat4 [Th1], Ccr4, and Gata3 [Th2], andlow levels of Foxp3 [iTreg]). Expression of genes from the IL10 modulepossibly represent a self-limiting mechanism, which is active in asubset of the cells and might play a role in the ‘non-pathogenic’effects of TH17 cells differentiated with Tgfβ1. Expression from other Tcell subsets may represent a contamination of the sample with non-Th17cells or, rather reflect a more complex picture of “hybrid” doublepositive cells.

High-throughput, high resolution, flow RNA-fluorescence in situhybridization (RNA-FlowFISH), an amplification-free imaging technique,was performed to verify that heterogeneity in the single-cell expressiondata reflected true biological differences, rather than librarypreparation biases and technical noise associated with the amplificationof small amounts of cellular RNA. For 9 genes, selected to cover a widerange of expression and variation levels, the heterogeneity detected byRNA-FLowFISH closely mirrored the sequencing data. For example,expression of housekeeping genes (such as β-actin (Actb) andβ2-microglobulin (B2m)) and key Th17 transcription factors (e.g., Rorc,Irf4, Batf) matched a log-normal distribution in both single-cellRNA-Seq and RNA-FISH measurements. By contrast, other signature genes(e.g., IL17a, IL2) showed significantly greater levels of heterogeneity,recapitulating the RNA-SEQ results.

Identification of Cell Sub-Populations:

To quantify this behavior, a model by Shalek et al. (Shalek, et al.“Single-cell transcriptomics reveals bimodality in expression andsplicing in immune cells.” Nature 2013 May 19. doi: 10.1038/nature1217),describing the distribution of a given gene across cells using threeparameters: (alpha)—the % of expressing cells; (sigma): the standarddeviation of expression for the expressing cells; and (Mu): the averagelevel of expression for expressing cells, was adapted. In this adaptedmodel, these parameters are inferred by fitting the expressiondistribution with a mixture-model of two distributions: a log normaldistribution for expressing cells and an exponential for non-expressingcells. Interestingly, it was found that the signature cytokine IL17aexhibited one of the highest levels of variability in the cell'stranscriptome in-vitro. Additional cytokines, chemokines and theirreceptors, including Ccl20, IL2, IL10, IL9 and IL24, were among thehighly variable genes. While these key genes exhibit strong variability,it was not clear to what extent these patterns are informative for thecell's state. To investigate this, the correlation between signaturegenes of various CD4+ lineages and all other expressed genes wascomputed. Clustering this map reveals a clear distinction betweenregulatory cytokines (IL10 module) and pro-inflammatory molecules (IL17,Rorc). Expression from the IL10 module possibly represents aself-limiting mechanism, which is active in a subset of the cells andplays a role in the ‘non-pathogenic’ effects of TH17 cellsdifferentiated with Tgfβ1.

To investigate this, principle component analysis was conducted on thespace of cells. It was found that the PCA can adequately separate IL17aexpressing cells from cells that did not express IL17a. In addition, itwas found that the first PC positively correlated with IL17a andnegatively correlated with IL10. The depiction of the cells in the spaceof the first two PC therefore spans the spectrum between potentiallypathogenic cells (high levels of IL-17a and low levels ofimmunosuppressive cytokines like IL-10) to non-pathogenic cells(opposite expression profiles). The PCs were characterized by computingcorrelations with other cell properties.

GPR65 Promotes Th17 Differentiation and Suppresses IL2:

A first set of experiments identified the target gene GPR65, aglycosphingolipid receptor that is genetically associated withautoimmune disorders such as multiple sclerosis, ankylosing spondylitis,inflammatory bowel disease, and Crohn's disease. GPR65 has shown apositive correlation with the module of genes associated with aninflammatory response, referred to herein as the IL17 module, andnegatively correlated with the module of genes associated with aregulatory cytokine profile, referred to herein as the IL10. The IL17module includes genes such as BATF, STAT4, MINA, IL17F, CTLA4, ZBTB32(PLZP), IL2, IL17A, and RORC. The IL10 module includes genes such asIL10, IRF4, IL9, IL24, and SMAD3. Genes that are known to have apositive correlation with the IL17 module include BATF, HIF1A, RORC, andMINA. Genes that are known to have a negative correlation with the IL17module include FOXP3, AHR, TRP53, IKZF3, IRF4, IRF1, IL10, IL23, andIL9. As described throughout the disclosure, novel regulators of theIL17 module include DEC1, CD5L, and ZBTB32 (PLZP).

To explore the role of GPR65, GPR65−/− mice were obtained anddifferentiated naïve T-cells under various T cell conditions (Th0, T16,T36, B623, T). FIGS. 40A and 40B demonstrate that IL17A expression isreduced in GPR65 knock out cells, for example, in FIG. 40A by 42% forT16 condition, by 48% for T36 condition, and by 73% for B623 condition,and in FIG. 40B by 20% in T16 condition and 13% for T36 condition. Inaddition, the B623 condition showed increased interferon gamma (IFNγ)production, a cytokine that is normally attributed to Th1 cells, andassociated with eliciting a severe immune response. These resultsdemonstrate that GPR65 is a regulator of Th17 differentiation. Thus,modulation of GPR65 can be used to influence a population of T cellstoward or away from a Th17 phenotype.

A second set of experiments identified the target gene DEC1 also knownas Bhlhe40. DEC1 is a basic helix-loop-helix transcription factor thatis known to be highly induced in a CD28-dependent manner upon T cellactivation (Martinez-Llordella et al. “CD28-inducible transcriptionfactor DEC1 is required for efficient autoreactive CD4+ T cellresponse.” J Exp Med. 2013 Jul. 29; 210(8):1603-19. doi:10.1084/jem.20122387. Epub 2013 Jul. 22). DEC1 is required for thedevelopment of experimental autoimmune encephalomyelitis and plays acritical role in the production of the proinflammatory cytokines GM-CSF,IFNγ, and IL-2 (Bluestone, 2013). Prior to the studies presented herein,DEC1 was not previously known to be associated with T cells generally,or with Th17 cells in particular.

To explore the role of DEC1, DEC1−/− mice were obtained anddifferentiated naïve T-cells under various T cell conditions (Th0, T16,T36, B623, T). As shown in FIG. 41A, IL-17A expression was unchanged inthe non-pathogenic condition, i.e., T16, but expression was reduced inthe pathogenic conditions T36 and B623, e.g., about 55% decrease for T36condition and about 43% decrease for B623 condition. As shown in FIG.41B, the DEC1 knockout cells also demonstrated an increase in FOXP3positive cells. FIG. 41C demonstrates that the cytokine secretion assay(CBA) largely supports the ICC data seen in FIG. 41A by demonstrating adecrease for IL17A for all Th17 conditions and an increase in IL-10production for all Th17 conditions. These results demonstrate that DEC1is a promoter of pathogenic Th17 differentiation. Thus, modulation ofDEC1 can be used to influence a population of T cells toward or awayfrom the Th17 pathogenic phenotype.

A third set of experiments identified the target gene PLZP also known asZbtb32. PLZP is a transcription factor that is known to be a repressorof GATA-3. PLZP has been shown to negatively regulate T-cell activation(I-Cheng Ho, 2004) and to regulate cytokine expression activation (SCMiaw, 2000).

To explore the role of PLZP, PLZP−/− mice were obtained anddifferentiated naïve T-cells under various T cell conditions (Th0, T16,T36, B623, T). As shown in FIG. 42A, IL-17A production was decreased inthe pathogenic Th17 cell conditions T36 and B623. These resultsdemonstrate that PLZP is a promoter of pathogenic Th17 differentiation.Thus, modulation of PLZP can be used to influence a population of Tcells toward or away from the Th17 pathogenic phenotype.

A fourth set of experiments identified the target gene TCF4(transcription factor 4), a basis helix-loop-helix transcription factor.TCF4 is known to be related to super-pathways including the MAPKsignaling pathway and the myogenesis pathway.

To explore the role of TCF4, TCF4−/− mice were obtained anddifferentiated naïve T-cells under various T cell conditions (Th0, T16,T36, B623, T). As shown in FIG. 43, IL-17A production was decreased inthe pathogenic Th17 cell condition B623. These results demonstrate thatTCF4 can be used as a promoter of pathogenic Th17 differentiation. Thus,modulation of TCF4 can be used to influence a population of T cellstoward or away from the Th17 pathogenic phenotype.

Example 10: CD5L, a Regulator of Intracellular Lipid Metabolism,Restrains Pathogenicity of Th17 Cells

IL-17-producing Th17 cells are present at the sites of tissueinflammation and have been implicated in the pathogenesis of a number ofautoimmune diseases in humans and relevant murine models (Kleinewietfeldand Hafler 2013, Lee, Collins et al. 2014). However, not all IL-17producing Th17 cells induce autoimmune tissue inflammation and disease(‘pathogenic’). Th17 cells that line the normal gut mucosa are thoughtto play an important role in tissue homeostasis by preventing tissueinvasion of gut microflora and promoting epithelial barrier functions(Guglani and Khader 2010). In addition, Th17 cells play a crucial rolein host defence against pathogens such as fungi (Candida albicans) andextracellular bacteria (Staphalococcus aureus) (Gaffen, Hernandez-Santoset al. 2011, Romani 2011). Therefore, Th17 cells show a great degree ofdiversity in their function: on one hand, they are potent inducers oftissue inflammation and autoimmunity, and on the other hand, theypromote tissue homeostasis and barrier function. The extracellularsignals and intracellular mechanisms that control these opposingfunctions of Th17 cells in vivo are only partially known and intensivelystudied.

Different types of Th17 cells with distinct effector functions can begenerated in vitro by different combination of cytokines. It has beenshown (Bettelli, Carrier et al. 2006; Veldhoen, Hocking et al. 2006;Harrington et al., 2006) that two cytokines, IL-6 and TGFβ1, can inducedifferentiation of naïve T cells into Th17 cells in vitro, althoughthese cells are poor inducers of Experimental AutoimmuneEncephalomyelitis (EAE), an autoimmune disease model of the centralnervous system. Exposure of these cells to the proinflammatory cytokineIL-23 can make them into disease-inducing, pathogenic, cells (McGeachy,Bak-Jensen et al. 2007, Awasthi, Riol-Blanco et al. 2009, Jager,Dardalhon et al. 2009, McGeachy, Chen et al. 2009). Indeed, othercombinations of cytokines, such as IL-1β+IL-6+IL-23 (Ghoreschi, Laurenceet al. 2010) or TGFβ3+IL-6+IL-23, can induce differentiation of Th17cells that elicit potent EAE with severe tissue inflammation uponadoptive transfer in vivo. Comparison of gene expression profiles ofTh17 cells generated with these distinct in vitro differentiationprotocols led to the identification of a gene signature thatdistinguishes pathogenic from non-pathogenic Th17 cells, consisting of aproinflammatory module of 16 genes expressed in pathogenic Th17 cells(e.g., T-bet, GMCSF and IL-23R) and a regulatory module of 7 genesexpressed in non-pathogenic cells (e.g., IL-10). Exposure ofnon-pathogenic Th17 cells to IL-23 converts them into a pathogenicphenotype, with the diminished expression of the regulatory module andthe induced expression of the proinflammatory module, suggesting thatIL-23 is a master cytokine that dictates the functional phenotype ofTh17 cells.

In humans, two different subtypes of Th17 cells have also been describedwith specificity for different types of pathogens. Th17 cells thatco-produce IL-17 with IFNγ were generated in response to Candidaalbicans, whereas Th17 cells that co-produce IL-17 with IL-10 havespecificity for Staphylococcus aureus infection (Zielinski, Mele etal.). Both IL-1 and IL-23 contributed to the induction of each of thesefunctionally-distinct subtypes of Th17 cells in response to antigen.Comparison of these human Th17 cell subsets with pathogenic andnon-pathogenic Th17 cells in mice suggest that the C. albicans-specificTh17 cells may mirror the pathogenic Th17 cells, with expression of theproinflammatory module, whereas S. aureus-specific Th17 cells are moresimilar to the non-pathogenic Th17 cells that has been described in themouse models of autoimmunity.

Identifying the key molecular switches that drive pathogenic andnon-pathogenic Th17 cells will allow selective inhibition of pathogenicTh17 cells, while sparing non-pathogenic, potentially tissue-protective,Th17 cells. To date, the intracellular mechanisms by which IL-23 evokesthe pathogenic phenotype in differentiating Th17 cells is not wellunderstood. Genomic approaches provide a compelling unbiased approach tofind such candidate mechanisms (Yosef et al. 2014), but it is likelythat pathogenic and non-pathogenic cells co-exist in vivo, andco-differentiate in vitro, limiting the power to detect subtler signals.Indeed, previous signature comparing populations of pathogenic andnon-pathogenic-derived cells did not find strong candidate regulators,but rather effector molecules. The advent of single cell RNA-Seq opensthe way to identify such subtler, yet physiologically important,regulators.

Here, single-cell RNA-Seq profiles of Th17 cells from in vivo autoimmunelesions and from in vitro differentiation were used to identify a novelregulator of Th17 pathogenicity, CD5L (CD5-Like). CD5L is predominantlyexpressed in non-pathogenic Th17 cells and is down-regulated uponexposure to IL-23. CD5L deficiency converts non-pathogenic Th17 cellsinto disease-inducing pathogenic Th17 cells, by regulating the Th17 celllipidome, altering the balance between polyunsaturated fatty acyls(PUFA) and saturated lipids, and in turn affecting the activity andbinding of Rorγt, the master transcription factor of Th17 celldifferentiation. Thus, CD5L is now identified as a critical regulatorthat distinguishes Th17 cell functional states, and T-cell lipidmetabolism as an integral component of the pathways regulating thepathogenicity of Th17 cells.

Results:

Th17 cells play a critical role in host defense against extracellularpathogens and maintenance of gut tissue homeostasis, but have also beenimplicated in the pathogenic induction of multiple autoimmune diseases.The mechanisms implicated in balancing such ‘pathogenic’ and‘non-pathogenic’ Th17 cell states remain largely unknown. Here,single-cell RNA-Seq was used to identify CD5L (CD5-Like) as one of thenovel regulators that is selectively expressed in non-pathogenic but notin pathogenic Th17 cells. While CD5L does not affect Th17differentiation, it serves as a major functional switch, as loss of CD5Lconverts ‘non-pathogenic’ Th17 cells into ‘pathogenic’ Th17 cells thatpromote autoimmune disease in mice in vivo. It is shown that CD5Lmediates this effect by modulating the intracellular lipidome, such thatTh17 cells deficient in CD5L show increased expression of saturatedlipids, including cholesterol metabolites, and decreased expression ofpoly unsaturated fatty acyls (PUFA). This in turn alters the ligandavailability to and function of Rorγt, the master transcription factorof Th17 cells, and T cell function. This study identified CD5L as acritical regulator of the functional state of Th17 cells and highlightedthe importance of lipid saturation and lipid metabolism in balancingimmune protection and disease in T cells.

Single-Cell RNA-Seq Identifies CD5L as a High-Ranking CandidateRegulator of Pathogenicity:

To identify candidate regulators of Th17 cell function, single-cellRNA-Seq profiles were analyzed from Th17 cells isolated from the CNSduring EAE in vivo or differentiated in vitro under non-pathogenic(TGFβ1+IL-6) and pathogenic (IL-1β+IL-6+IL-23) conditions. Briefly threelines of evidence were used to rank genes for their potentialassociation with pathogenicity: (1) co-variation analysis of atranscript's expression across single Th17 cells differentiated in vitro(in the non-pathogenic conditions), which showed the presence of twoanti-correlated modules: a “pro-inflammatory module” (positivelycorrelated with the expression of Il17a) and a “regulatory module”(positively correlated with the expression of Il10); (2) PrincipleComponents Analysis (PCA) of single Th17 cells differentiated undereither condition, which showed that cells span a pathogenicity spectrum,such that a cell's location on PC1 is related to the expression ofpathogenic genes; and (3) PCA of single Th17 isolated from the CNS andlymph node during EAE in vivo, which showed that cells span a widefunctional spectrum along the first PC (from effector to memory toexhausted state) and the second PC (from a naïve-like to terminallydifferentiated state).

Cd5l (Cd5-like) was one of the high-ranking genes by single-cellanalysis of potential regulators, showing a surprising combination oftwo key features: (1) it is only expressed in vitro in Th17 cellsderived under non-pathogenic conditions (FIG. 45D); but (2) in thosenon-pathogenic cells, its was expressed as a member in co-variance withthe other genes in the proinflammatory module in Th17 cells. First, thevast majority (˜80%) of Th17 cells derived under the pathogeniccondition (IL-1β+IL-6+IL-23) lacked Cd5l expression, whereas Th17 cellsdifferentiated under the non-pathogenic (TGF-b1+IL-6) conditionpredominantly expressed Cd5l (FIG. 45C). Furthermore, most of sortedIL-17A+(GFP+, where GFP is under the control of IL-17 promoter) cellsdifferentiated under the non-pathogenic condition (TGFβ1+IL-6) expressedCd5l (FIG. 45D, top left panel), consistent with its originalassociation with the IL17 module (in non-sorted cells; below). Incontrast, Th17 cells differentiated under two different pathogenicconditions (IL-1+IL-6+IL-23 or TGFβ3+IL-6) lacked Cd5l expression in amajority of the T cells. Similarly, none of the encephalitogenic Th17cells (CD4⁺ IL-17A.GFP⁺) sorted from the central nervous system (CNS) ofmice undergoing active EAE expressed any Cd5l at the single-cell level(FIG. 45D, lower right panel). Second, CD5L is highly positivelycorrelated with the defining signature of the pro-inflammatory model,and negatively correlated with the regulatory module. In particular, itis among the top 5 genes in the proinflammatory module whose expressionis also most strongly correlated with the expression ofpreviously-defined pathogenic gene signature (FIG. 45A, empiricalp-value<0.05). Furthermore, non-pathogenic Th17 cells expressing higherlevels of Cd5l also have lower scores for the aforementioned PC1, asdoes the pathogenicity signature (FIG. 45B, Pearson correlation of 0.44;p<10⁻⁷).

CD5L is a member of the scavenger receptor cysteine rich superfamily(Sarrias M R et al. 2004). Its expression was previously reported inmacrophages (Miyazaki, Hirokami et al. 1999), and it has been shown tobind to cytosolic fatty acid synthase in adipocytes followingendocytosis. It has also been reported to be a receptor for PathogenAssociated Molecular Patterns (PAMPs), and may have a function inregulating innate immune responses (Martinez V G et al. 2014). However,it has not been reported to be expressed in T cell and therefore it'srole in T cell function has not been identified.

CD5L expression is specifically associated with non-pathogenic Th17cells in vitro and in vivo: It was hypothesized that CD5L's exclusiveexpression in Th17 cells differentiated under non-pathogenic conditionsbut in association with the IL17 inflammatory module, may indicate aunique role in regulating the transition between a non-pathogenic andpathogenic state. While co-expression with the inflammatory module andcorrelation with a pathogenicity signature (FIG. 45A,B) per se couldhave suggested a function as a positive regulator of pathogenicity, theapparent absent of CD5L from Th17 cell differentiated in vitro underpathogenic conditions or isolated from lesions in the CNS (FIG. 45C,D)suggest a more nuanced role. In particular, it was hypothesized thatCD5L may be a negative regulator of pathogenicity, explaining itsabsence from truly pathogenic cells. Notably, mRNAs of negativeregulators of state-changes in cells are often co-regulated with themodules that they negatively regulate in eukaryotes from yeast (Segal etal., Nature Genetics 2003; Pe'er et al. Bioinformatics 2002) to human(Amit et al Nature Genetics 2007).

To test this hypothesis, the initial finding that CD5L is uniquelyexpressed in non-pathogenic Th17 cells both in vitro and in vivo withqPCR (FIG. 45E, F) and protein expression analyses (FIG. 45G) of naïveCD4 T cells cultured under various differentiation conditions was firstvalidated and extended. At the mRNA level, little to no Cd5l expressionwas found in Th0, Th1 or Th2 helper T cells (FIG. 45E), high expressionin Th17 cells differentiated with TGFβ1+IL-6, but little to noexpression in Th17 cells differentiated IL-1β+IL-6+IL-23 or in iTregs(FIG. 45E). Importantly, similar patterns are observed for CD5L proteinexpression by flow cytometry (FIG. 45G).

Next, it was explored whether CD5L expression is associated with lesspathogenic Th17 cells in vivo. First, Cd5l expression was analyzed inTh17 cells isolated from mice following immunization with myelinoligodendrocyte glycoprotein (MOG₃₅₋₅₅) in complete Freund's adjuvant(CFA). Th17 cells (CD3⁺CD4⁺IL-17.GFP⁺) were sorted from the periphery(spleen) and it was found that Cd5l was only expressed in IL-17⁺ but notIL-17⁻ T cells (FIG. 45H, left panel). In striking contrast, Cd5l wasnot expressed in Th17 cells from the CNS despite significant expressionof Il17 (FIG. 45H, right panel), consistent with the single-cell RNA-seqdata (FIG. 45D). Next, Cd5l expression was analyzed on Th17 cellsisolated from naïve mice that line the gut mucosa and are not associatedwith inflammation. IL-17A.GFP⁺ and IL-17A.GFP⁻ CD4 T cells were isolatedfrom the mesenteric lymph node (mLN) and the lamina propria (LP) ofnaïve mice, where Th17 cells are thought to contribute to tissuehomeostasis and mucosal barrier function. IL-17⁺ but not IL-17⁻ T cellsharvested from mLN and LP of normal gut mucosa expressed high levels ofCd5l (FIG. 45I and data not shown). Thus, CD5L is a gene expressed innon-pathogenic (but not in pathogenic) Th17 cells in vivo.

Finally, it was tested whether IL-23 exposure, known to make Th17 cellsmore pathogenic, can directly regulate Cd5l expression. It washypothesized that if CD5L is a positive regulator of IL23-dependentpathogenicity its expression will be increased by IL23, whereas if it isa negative regulator, its expression will be suppressed. As IL-23R isinduced after 48 hours of T-cell activation, naïve T cells weredifferentiated with TGFβ1+IL-6 for 48h and then expanded with or withoutIL-23 in fresh media. The addition of IL-23 significantly suppressedCd5l expression as compared to PBS control (FIG. 45F), consistent withthese cells acquiring a pro-inflammatory module and becoming pathogenicTh17 cells, and with the hypothetical assignment of CD5L as a negativeregulator of pathogenicity.

CD5L Represses/Dampens Th17 Cell Effector Function without AffectingTh17 Differentiation of Naïve T Cells:

To analyze whether CD5L plays any functional role in vivo, wildtype (WT)and Cd5l deficient mice were immunized with MOG₃₅₋₅₅/CFA to induce EAE.CD5L^(−/−) mice exhibited significantly more severe clinical EAE thatpersisted for at least 28 days, whereas WT mice began recovering 12 dayspost immunization (FIG. 46A). Next, the phenotype of CD4 T cells wasanalyzed during the course of EAE. Similar frequencies of FoxP3+ Tregcells were found in WT and CD5L^(−/−) mice, suggesting that theincreased severity of the disease was not due to a decreased number ofTregs in Cd5l deficient mice (FIG. 50A). On the other hand, asignificantly higher percentage of IL-17-producing CD4 T cells and alower percentage of IFNγ⁺ CD4 T cells in the CNS of CD5L^(−/−) mice(FIGS. 46A and 51B) was observed. Moreover, in response to MOGreactivation in vitro, cells from the draining lymph node (dLN) ofCD5L^(−/−) mice showed higher proliferative responses and produced moreIL-17 (FIG. 51C, D). This is consistent with either a direct or indirectrole for CD5L in defining the function of Th17 cells.

To determine whether CD5L's effect is due to a direct role in thedifferentiation of Th17 cells, naïve WT and CD5L^(−/−) CD4 T cells wereanalyzed under the non-pathogenic Th17 cell condition and analyzedwhether CD5L directly regulated the expression of signature Th17 genes.The loss of CD5L did not affect Th17 differentiation of naïve T cells,as measured by IL-17 expression by intracellular cytokine staining or byELISA (FIG. 46B, C), nor that of other signature Th17 genes includingIl17f, Il21, Il23r or Rorc (FIG. 46D). However, under the non-pathogenicTh17 differentiation condition, WT Th17 cells produce IL-10, whereasCD5L^(−/−) Th17 cells showed a decrease in the expression of IL-10 asdetermined by ELISA (FIG. 46C) or qPCR analysis (FIG. 46D). Theseobservations suggest that CD5L does not regulate Th17 celldifferentiation directly, that Th17 cell differentiation alone cannotexplain the increased susceptibility to EAE in CD5L^(−/−) mice, but thatCD5L may indeed affect the internal state of differentiated Th17 cells.

Next, it was determined whether CD5L has any role in expanding ormaintaining effector/memory Th17 cells. To this end, naïve Th17 cellsdifferentiated under the non-pathogenic conditions were washed andre-plated without IL-23. Upon restimulation, the CD5L^(−/−) Th17 cellshad a significantly higher percentage of IL-17A⁺ cells and IL-23R⁺ cells(FIG. 46E), suggesting that CD5L deficiency leads to more stablyexpanding Th17 cells. Consistent with this result, CD5L^(−/−) Th17 cellsexpressed more Il17a and Il123r and less Il10 as determined by qPCR(FIG. 46F). Thus, CD5L does not regulate initial Th17 celldifferentiation of the naïve T cells but does control their expansionand/or effector functions over time. Consistent with this result,effector memory cells (CD4⁺CD62L⁻CD44⁺) isolated directly ex vivo fromCD5L^(−/−) mice expressed significantly higher IL-17 and lower IL-10levels (FIG. 46G). This higher percentage of effector memory T cellsproducing IL-17 might reflect the greater stability and higher frequencyof Th17 cells that persist in the repertoire of CD5L^(−/−) mice. Toaddress whether Th17 cells isolated in vivo also produced more IL-17 ona per cell basis, RORγt⁺ (GFP⁺) effector/memory T cells were sorted fromWT and CD5L^(−/−) mice, their cytokine production upon activation exvivo was analyzed. The RORγt.GFP⁺ T cells from the CD5L^(−/−) miceshowed much higher production of IL-17 and lower production of IL-10suggesting that RORγt⁺ cells are better IL-17 producers in the absenceof CD5L (FIG. 46H).

CD5L is a Major Switch that Regulates Pathogenicity of Th17 Cells:

To study whether loss of CD5L can convert non-pathogenic Th17 cells intopathogenic, disease-inducing Th17 cells, CD5L^(−/−) mice were crossed to2D2 transgenic mice that express TCRs specific for MOG35-55/IA^(b).Naïve 2D2 transgenic T cells carrying CD5L deficiency weredifferentiated under the non-pathogenic (TGFβ1+IL-6) Th17 condition andthen transferred into WT recipients. Prior to transfer, a similarfrequency of IL-17⁺ T cells was generated from WT and CD5L^(−/−) 2 D2naïve cells (FIG. 47A), consistent with the observation that CD5L doesnot affect Th17 differentiation of naïve T cells.

Next, clinical and histological disease progression in the recipients ofWT and CD5L^(−/−) 2 D2 cells was compared. As expected, many recipients(6/13) of WT 2D2 Th17 cells showed very little to no signs of clinicalor histological EAE. Strikingly, all (12/12) CD5L^(−/−) 2 D2 recipientsdeveloped severe EAE with optic neuritis. Moreover, CD5L^(−/−) 2 D2recipients had significant weight loss and developed more ectopiclymphoid follicle-like structures in the CNS, a hallmark of diseaseinduced by highly pathogenic IL-23-treated Th17 cells (FIG. 47B, C)(Peters, Pitcher et al. 2011). Thus, T cell intrinsic expression of CD5Lplays a pivotal role in restraining the pathogenicity of Th17 cells.After adoptive transfer, the T cells were isolated from the CNS of miceundergoing EAE. The 2D2 CD5L^(−/−) T cells retained a much higherfrequency of IL-17 producing T cells and a reduced level of IL-10 ascompared to the WT 2D2 T cells (FIG. 47D). Upon adoptive transfer, WT2D2 T cells acquired production of IFNγ in vivo, whereas only a verysmall proportion of CD5L^(−/−) 2 D2 T cells produced IFNγ, suggestingthat CD5L may also regulate the stability of Th17 cells. Consistent withthis observation, when the naïve WT and CD5L^(−/−) 2 D2 T cells weretransferred into WT hosts and immunized the mice with MOG₃₅₋₅₅/CFAwithout inducing EAE (no pertussis toxin was given), CD5L^(−/−) 2 D2 Tcells accumulated a higher frequency of IL-17A⁺ T cells compared to WT.Strikingly, while the WT T cells expressed IL-10, none of the CD5L^(−/−)2 D2 T cells expressed IL-10 (FIG. 47E).

As IL-23 can suppress the expression of CD5L, and since CD5L functionsto restrain Th17 cell pathogenicity, it was reasoned that sustained CD5Lexpression should antagonize the IL-23 driven pathogenicity of Th17cells. To test this hypothesis, a retroviral vector for ectopicexpression of CD5L in Th17 cells was generated. Naïve 2D2 T cells weredifferentiated under pathogenic differentiation conditions(IL-1β+IL-6+IL-23), transduced with CD5L, transferred into WT recipientsand followed for weight loss and the development of clinical EAE. Priorto transfer, 2D2 T cells transduced with CD5L had similar IL-17expression and increased IL-10 expression (FIG. 51A). After transfer,ectopic expression of CD5L in Th17 cells differentiated under pathogenicconditions reduced their pathogenicity when compared to the WT controlin that they led to reduced weight loss in mice and a significantdecrease in the induction of EAE (FIG. 51B, C). Furthermore, CD5Lover-expressing 2D2 T cells transferred in vivo, lost IL-17 productionand most of the transferred cells began producing IFNγ (FIG. 51D).Therefore, CD5L does not regulate Th17 differentiation of naïve T cells,but affects the functional state of Th17 cells in that the loss of CD5Lconverts non-pathogenic Th17 cells into pathogenic Th17 cells thatstably produce IL-17 in vivo and its sustained over-expression inpathogenic Th17 cells converts them to a less pathogenic and less stablephenotype in that these cells lose the expression of IL-17 and acquirean IFNγ producing Th1 phenotype in vivo. These two data setsunequivocally support the role of CD5L as a negative regulator of thefunctional pathogenic state of Th17 cells.

Consistent with these functional findings, CD5L also regulates theexpression of the pathogenic/non-pathogenic gene signature previouslydefined in Th17 cells. To show this, naïve WT and CD5L^(−/−) T cellswere differentiated under the non-pathogenic TGFβ1+IL-6 condition andrested them in fresh media without adding any exogenous IL-23 for 48hours followed by mRNA expression analysis by qPCR. CD5L deficient Th17cells differentiated under the non-pathogenic condition significantlyupregulated several effector molecules of the pathogenic signature,including Il123r, Il3, Ccl4, Gzmb, Lrmp, Lag3 and Sgk1, anddownregulated several genes of the non-pathogenic signature, includingIl10, Il9 and Maf (FIG. 47F). Several other signature genes, however,were not affected by CD5L, suggesting a more nuanced mechanism.

CD5L Shifts the Th17 Cell Lipidome Balance from Saturated to UnsaturatedLipids, Modulating Rorγt Ligand Availability and Function:

Since CD5L is known to regulate lipid metabolism, by binding to fattyacid synthase in the cytoplasm of adipocytes (Kurokawa, Arai et al.2010), it was speculated that CD5L may also regulate Th17-cell functionby specifically regulating lipid metabolites in T cells. To test thishypothesis, it was analyzed whether lipid metabolism is regulated byCD5L and is associated with the increased pathogenicity observed in Th17cells from CD5L^(−/−) mice. The lipidome of WT and CD5L^(−/−) Th17 cellsdifferentiated under the non-pathogenic (TGFβ1+IL-6) and pathogenic(TGFβ1+IL-6+IL-23) conditions was profiled. It was possible to resolveand identify around 200 lipid metabolites intracellularly or in thesupernatant of differentiating Th17 cells using mass spectrometry andliquid chromatography. Of those metabolites that were differentiallyexpressed between WT and CD5L^(−/−), a striking similarity between thelipidome of CD5L^(−/−) Th17 cells differentiated under thenon-pathogenic condition and WT Th17 cells differentiated under thepathogenic condition (FIG. 48A) was observed. Among other metabolicchanges, CD5L deficiency significantly increased the levels of saturatedlipids (SFA), including metabolites that carry saturated fatty acyl andcholesterol ester (CE) as measured by liquid chromatography and massspectrometry (FIGS. 48B and 52A), and free cholesterol as shown bymicroscopy (FIG. 52B). Moreover, the absence of CD5L resulted in asignificant reduction in metabolites carrying poly-unsaturated fattyacyls (PUFA) (FIG. 48B). Similar increase in CE and reduction in PUFA isobserved in the lipidome of Th17 cells differentiated under either oftwo pathogenic conditions (IL-1β+IL-6+IL-23 and TGFβ3+IL-6+IL-23)compared to non-pathogenic WT cells (FIG. 48C and FIG. 51A). Thus, Th17cell pathogenicity is associated with a shift in the balance of lipidomesaturation as reflected in the increase in saturated lipids and decreasein PUFA metabolites.

Cholesterol metabolites, such as oxysterols, have been previouslyreported to function as agonistic ligands of Rorγt (Jin, Martynowski etal. 2010, Soroosh, Wu et al. 2014). Previous ChIP-Seq analysis (Xiao,Yosef et al. 2014) suggests that Rorγt binds at several sites in thepromoter and intronic regions of Il23r and Il17 (FIG. 48D) and nearCNS-9 of Il10, where other transcription factors, such as cMaf, whichregulates Il10 expression, also binds. As showed above, CD5L restrainsthe expression of IL-23R and IL-17 and promotes IL-10 production inRorγt⁺ Th17 cells, and because CD5L-deficient Th17 cells contain highercholesterol metabolite and lower PUFA (FIG. 48A,B). Putting these datatogether, it was hypothesized that CD5L regulates the expression ofIL-23R, IL-17 and IL-10 by affecting the binding of Rorγt to thesetargets, through affecting the SFA-PUFA balance.

To test this hypothesis, it was first assessed if CD5L modulates Rorγtactivity by using ChIP-PCR and luciferase reporter assays. Consistentwith the hypothesis, ChIP of Rorγt showed significantly higher bindingof Rorγt in the Il17 and Il23r region and significantly reduced bindingto the Il10 region in CD5L-deficient Th17 cells compared to WT (FIGS.48D,E and 52C). Consistently, ectopic overexpression of CD5L issufficient to suppress Rorγt-dependent transcription of Il17 and Il23rpromoter luciferase reporters (FIGS. 48F and 52D) and to enhance thetranscription of the Il10 reporter in the presence of Rorγt (FIG. 48G).

Next, it was tested whether changing the lipidome balance of WT Th17cells with the addition of SFA or PUFA can regulate Rorγt binding togenomic regions (FIGS. 48DE and 52C), finding that in the presence ofSFA, there is a significant increase in the enrichment of Rorγt-bindingto Il17 and Il23r genomic elements, whereas there was a decrease in thebinding of Rorγt in the presence of PUFA (FIGS. 48D and 52C). Additionof PUFA also significantly increased the enrichment of Rorγt binding tothe Il10 CNS-9 region (FIG. 48E), suggesting that manipulation of thelipid content of Th17 cells can indeed modulate Rorγt DNA bindingability.

Finally, it was reasoned that if CD5L regulates Rorγt transcriptionalactivity by limiting Rorγt ligand(s), the addition of exogenous agonistsof Rorγt would rescue the CD5L induced suppression. Indeed, addition of7, 27 dihydroxycholesterol, previously shown as an endogenous ligand ofRorγt (Soroosh, Wu et al. 2014), rescued the CD5L-driven suppression ofIl17 reporter transcription, suggesting ligand availability partlycontributes to the regulation of Rorγt function by CD5L (FIG. 48H). Onthe other hand, the addition of PUFA decreased Rorγt driven Il17atranscription in control cells, but not in those expressing CD5L (FIG.48I), suggesting the function of PUFA may depend on the Rorγt ligand.Indeed, while Rorγt can strongly transactivate Il23r enhancer in thepresence of an agonistic ligand, the addition of PUFA to the agonistligand almost completely inhibited Rorγt-mediated Il23r transactivationand enhanced Il10 transactivation (FIG. 48J,K). This observationsuggests that PUFA may modulate Rorγt ligand binding and thus affect theability of Rorγt to transactivate Il23r and Il10. On the other hand,while the addition of SFA by itself has little impact on Rorγt-dependenttranscription, it nevertheless modified the function of the oxysterol(FIG. 48J,K). Thus, CD5L regulates the expression of Il23r and Il10,members of the pathogenic/non-pathogenic signature, by shifting lipidomebalance and limiting Rorγt ligand availability as well as function.

PUFA and SFA can Regulate Th17 Cell Function and Contribute toCD5L-Dependent Regulation of Th17 Cells:

As CD5L-deficient Th17 cells differentiated under the non-pathogeniccondition have altered balance in lipid saturation, and since PUFA andSFA can modulate Rorγt binding and functional activity, the relevance offatty acid moeities to Th17 cell function and its contribution toCD5L-driven Th17 cell pathogenicity was analyzed. The effect of addingPUFA and SFA on the generation of Th17 cells was first tested. WT Th17cells were differentiated with TGFβ1+IL-6 and expanded using IL-23 infresh media with the presence of either PUFA or SFA. PUFA suppressed thepercentage of IL-17⁺ and IL-23R.GFP⁺ CD4 T cells (FIG. 49A), suggestingthat PUFA can limit Th17 cell function under the pathogenic condition.On the other hand, addition of SFA increased the expression of bothIL-17 and IL-23R expression, but this effect was not significant,possibly because the already very high levels of SFA in the pathogenicTh17 cells could not be further altered by the addition of exogenousSFA. This result is consistent with qPCR analysis of Il17 and Il23rexpression and further, the effect of PUFA is abolished in Rorγt^(−/−)Th17 cells (FIG. 49B), suggesting the function of PUFA requires Rorγtexpression. CD5L^(−/−) Th17 cells differentiated under thenon-pathogenic condition are also sensitive to PUFA treatment, resultingin reduced percentage of IL-17+CD4+ T cells (FIG. 49C).

Next, the contribution of lipid saturation to Th17 cell pathogenicitywas studied. It was speculated that if the balance of lipid saturationdistinguishes non-pathogenic WT Th17 cells and pathogenic CD5L^(−/−)Th17 cells, the addition of SFA to WT and PUFA to CD5L^(−/−) Th17 cells(TGFβ1+IL-6) can result in reciprocal changes in transcriptionalsignature relevant to Th17 cell pathogenicity. Therefore (using theNanostring nCounter) the expression of a 316 gene signature of Th17 celldifferentiation and function in SFA- or control-treated WT Th17 cellsand in PUFA- or control-treated CD5L^(−/−) Th17 cells differentiatedwith TGFβ1+IL-6 was analyzed. It was found that PUFA-treated CD5L^(−/−)Th17 cells resemble WT non-pathogenic Th17 cells, and SFA-treated WTnon-pathogenic Th17 cells are more similar to CD5L^(−/−) Th17 cells(FIG. 49D). qPCR analysis confirmed that PUFA and SFA reciprocallyregulated the expression of key genes in the pathogenicity signatures,including Il10, Il23r, Ccl5, Csf2 and Lag3 (FIG. 49D). (Notably, in somecases PUFA and SFA have the same effects; for example, Il22 expressionis increased following treatment by either fatty acid.) Taken together,these observations suggest that the balance of lipid saturationcontributes to CD5L-dependent regulation of Th17 cells by regulating theTh17 cell transcriptome.

Discussion:

Th17 cells are a T helper cell lineage capable of diverse functionsranging from maintaining gut homeostasis, mounting host defense againstpathogens, to inducing autoimmune diseases. How Th17 cells can mediatesuch diverse and opposing functions remains a critical question to beaddressed. This is especially important since anti-IL-17 and Th17-basedtherapies have been highly efficacious in some autoimmune diseases, buthave had no impact in other diseases (Genovese, Van den Bosch et al.2010, Hueber, Sands et al. 2012, Leonardi, Matheson et al. 2012, Papp,Leonardi et al. 2012, Baeten and Kuchroo 2013, Patel, Lee et al. 2013),even when Th17 cells have been genetically linked to the disease process(Cho 2008, Lees, Barrett et al. 2011). Using single-cell genomics thisissue has been addressed and identified novel functional regulators ofTh17 cells have been identified.

Here, CD5L is highlighted and investigated as one of the novelregulators that affects the pathogenicity of Th17 cells. It is shownthat: (1) CD5L is highly expressed only in non-pathogenic Th17 cells butin them co-varies with a pro-inflammatory module, a pattern consistentwith being a negative modulator of pathogenicity; (2) CD5L does notaffect Th17 differentiation but affects long-term expansion and thefunctional phenotype of Th17 cells; (3) CD5L-deficiency convertsnon-pathogenic Th17 cells into pathogenic Th17 cells; and (4) CD5Lregulates lipid metabolism in Th17 cells and alters the balance betweenSFA and PUFA.

Seemingly paradoxically, CD5L is expressed only in non-pathogenic Th17cells, but in co-variance with the pro-inflammatory module. This initialobservation led us to hypothesize that CD5L is a negative regulator of anon-pathogenic to pathogenic transition, since such negative regulatorsare often known to co-vary in regulatory networks with the targets theyrepress, in organisms from yeast. Functional analysis bears out thishypothesis, suggesting that CD5L might indeed be expressed to restrainthe pro-inflammatory module in the non-pathogenic Th17 cells. Thus,other genes with this specific pattern—exclusive expression innon-pathogenic cells but in co-variance with the pro-inflammatory modulemay also be repressors that quench pro-inflammatory effector functions.Thus, depending on the environmental context or trigger, non-pathogenicTh17 cells can be readily converted into pro-inflammatory or pathogenicTh17 cells, by inhibiting the expression of a single gene like CD5L.This is supported by the data, which clearly show that IL-23R signallingcan suppress CD5L expression and that the persistent expression of CD5Linhibits the pro-inflammatory function of Th17 cells. In addition tosuppressing the pro-inflammatory module, CD5L may also promote thefunction of the regulatory module, thereby acting as a switch to allowrapid responses to environmental triggers, such that Th17 cells canchange their functional phenotype without having to depend on otherintermediary pathways. It is also apparent that the expression of CD5Lcan stabilize the function of non-pathogenic Th17 cells, so that theregulatory module and proinflammatory module could co-exist in a cellpopulation. This observation also highlights the molecular differencebetween the regulatory module and the proinflammatory module that areco-expressed in non-pathogenic Th17 cells, suggesting that thenon-pathogenic Th17 cells that can produce both IL-17 and IL-10 have aunique role in physiological processes. This is consistent with therecent discovery that Th17 cells that can develop in the small intestinein response to gut microbiome (Esplugues, Huber et al. 2011), as well asthat Th17 cells that can also co-produce IL-10 and are presumablyimportant for protective immunity against S. aureus infection on themucosal surfaces of the lung (Zielinski, Mele et al.) do not mediateautoimmunity or tissue injury.

Both pathogenic and non-pathogenic Th17 cells are present in thedraining lymph nodes but pathogenic Th17 cells appear at the site oftissue inflammation (CNS) and non-pathogenic Th17 cells appear in thegut or other mucosal surfaces, where they promote mucosal barrierfunction and also maintain tissue homeostasis. This is mirrored in theexpression of CD5L, which is highly expressed in Th17 cells in the gutat the steady state, but not in the CNS at the peak of autoimmune tissueinflammation. IL-23, which is present in the CNS during EAE, cansuppress CD5L and convert non-pathogenic Th17 cells into pathogenic Th17cells. At the steady state, it is not known what promotes CD5Lexpression and non-pathogenicity in the gut. TGFβ is an obviouscandidate given the abundance of TGFβ in the intestine and its role inboth differentiation of IL-10 producing CD4 T cells in vivo (Maynard,Harrington et al. 2007, Konkel and Chen 2011) and the differentiation ofTh17 cells in vitro (Bettelli, Carrier et al. 2006, Veldhoen, Hocking etal. 2006). Specific commensal bacteria (Ivanov, Atarashi et al. 2009,Yang, Torchinsky et al. 2014) and metabolites from microbiota (Arpaia,Campbell et al. 2013) have also been implicated in regulating T celldifferentiation. Notably, CD5L is reported as a secreted protein(Miyazaki, Hirokami et al. 1999) and plays a role in recognizing PAMP(Martinez V G et al. 2014). It is possible that, in vivo, CD5L expressedby non-pathogenic Th17 cells in the gut can interact with the microbiotaand maintains gut tolerance and a non-pathogenic Th17 phenotype.Therefore, the two functional states of Th17 cells may be highlyplastic, and depending on the milieu, either pathogenic ornon-pathogenic Th17 cells can be generated by sensing changes in thetissue micro-environment. It is clear, however, the expression of CD5Lin non-pathogenic Th17 cells is critical for maintaining thenon-pathogenic functional state of Th17 cells and IL-23 rapidlysuppresses CD5L, which renders these cells pathogenic. This hypothesisalso predicts non-pathogenic Th17 cells can be easily converted intopathogenic Th17 cells by production of IL-23 locally in the gut duringinflammatory bowel disease.

How does CD5L regulate the pathogenicity of Th17 cells? In this study,evidence is provided that CD5L can regulate Th17 cell function at leastin part by regulating intracellular lipid metabolism in Th17 cells. CD5Lwas shown to inhibit the de novo synthesis of fatty acid through directbinding to fatty acid synthase (Kurokawa, Arai et al. 2010), althoughthis has not been demonstrated in T cells. It was discovered that inTh17 cells CD5L is not a general inhibitor of fatty acid synthesis, butregulates the balance of PUFA vs. SFA. It is shown that PUFA limitsligand-dependent function for Rorγt, such that in the presence of CD5Lor PUFA, Rorγt binding to the Il17a and Il23r is enhanced, along withreduced transactivation of both genes, whereas binding at and expressionfrom the Il10 locus is enhanced. Notably, Rorγt's ability to regulateIl10 expression was not reported previously. Since CD5L does not impactoverall Th17 cell differentiation, this suggests a highly nuanced effectof CD5L and lipid balance on Rorγt function, enhancing its binding toand transcactivation at some loci, reducing it in others, and likely notaffecting its function at other loci, such as those needed for generalTh17 cell differentiation. How this is achieved mechanistically remainsto be investigated. For example, the regulation of Il10 transcription iscomplex and depends on diverse transcription factors and epigeneticmodifications. In Th17 cells, Stat3 and c-Maf can promote the expressionof Il10 (Stumhofer, Silver et al. 2007, Xu, Yang et al. 2009). As Stat3,C-Maf and Rorγt can all bind to the same Il10 enhancer element, it istherefore possible that, depending on the quality and quantity of theavailable ligands, Rorγt may interact with other transcription factorsand regulate Il10 transcription. More generally, this supports ahypothesis where the spectrum of Rorγt ligands depends—at least inpart—on the CD5L-regulated PUFA vs. SFA lipid balance in the cell, andwhere different ligands impact distinct specificity on Rorγt, allowingit to assume a spectrum of functional states, related for example todistinct functional states. Further studies would be required to fullyelucidate such a mechanism.

Several metabolic pathways have been associated with Th17 celldifferentiation. HIF1a can promote Th17 cell differentiation throughdirect transactivation of Rorγt (Dang, Barbi et al. 2011, Shi, Wang etal. 2011) and acetyl-coA carboxylase can regulate Th17/Treg balancethrough the glycolytic and lipogenic pathway (Berod, Friedrich et al.2014). Both HIF1a and acetyl-coA carboxylase are associated with obesityand mice harbouring mutations in genes that regulate Th17 celldifferentiation and function have been shown to acquire an obesephenotype (Winer, Paltser et al. 2009, Ahmed and Gaffen 2010, Jhun, Yoonet al. 2012, Mathews, Wurmbrand et al. 2014). Thus, there appears to bean association between Th17 cell development and obesity. A hallmark ofobesity is the accumulation of saturated fat and cholesterol. In thisstudy, evidence is provided that at the cellular level, lipidomesaturation can promote Th17 cell function by regulating Rorγt function.

In addition to regulating the pathogenicity of Th17 cells, CD5Ldeficient Th17 cells appeared to retain a more stable Th17 phenotype invivo. Th17 cells from CD5L deficient naïve 2D2 T cells differentiatedunder non-pathogenic conditions remain mostly IL-17⁺ and IFNγ⁻ upontransfer into a WT host in contrast to WT 2D2 cells, which attain moreIFNγ⁺ expression. Moreover, transfer of undifferentiated naïveCD5L^(−/−) CD4⁺ 2D2 T cells resulted in higher frequency of IL-17A⁺cells following immunization as compared with WT 2D2 T cells. As CD5Ldoes not regulate Th17 cell differentiation of naïve T cells, thissuggests that the Th17 cellular phenotype may be more stable in theabsence of CD5L. It is possible that Th17 cell stability is in partdependent on ligand availability. Therefore, sensing of themicroenvironment by Th17 cells may change CD5L expression and regulateRorγt ligand availability, which in turn may affect Th17 phenotype andfunction.

Thus, by using single cell genomics and computational analysis, CD5L hasbeen identified as a novel repressor of pathogenicity of Th17 cells,highlighting the power of single cell genomics to identify molecularswitches that affect Th17 cell functions, otherwise obscured bypopulation-level genomic profiles. CD5L appears to be a molecular switchthat does not affect Th17 differentiation per se but one that impactsthe function (pathogenic vs. non-pathogenic phenotype) of Th17 cells,potentially by regulating the quality and/or quantity of available Rorγtligands, allowing a single master regulator to possibly assume multiplefunctional states. The results connect the lipidome to essentialfunctions of immune cells, opening new avenues for sensitive andspecific therapeutic intervention.

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Example 11: GPR65 Promotes Th17 Differentiation and is Essential for EAE

GPR65, a glycosphingolipid receptor, is co-expressed with thepro-inflammatory module (FIGS. 4B and S6E), suggesting that it mighthave a role in promoting pathogenicity. GPR65 is also highly expressedin the in vivo Th17 cells harvested from the CNS that attain a Th1-likeeffector/memory phenotype (FIG. 2D). Importantly, genetic variations inGPR65 are associated with multiple sclerosis (International MultipleSclerosis Genetics et al., 2011), ankylosing spondylitis (InternationalGenetics of Ankylosing Spondylitis et al., 2013), inflammatory boweldisease (Jostins et al., 2012), and Crohn's disease (Franke et al.,2010).

The role of GPR65 was tested in Th17 differentiation in vitro and in thedevelopment of autoimmunity in vivo. Naïve T-cells isolated fromGpr65^(−/−) mice in vitro were differentiated with TGF-β1+IL-6(non-pathogenic condition) or with IL-1β+IL-6+IL-23 (pathogeniccondition) for 96 hours. In both cases, there was a ˜40% reduction ofIL-17a positive cells in Gpr65^(−/−) cells compared to their wild typecontrols as measured by intracellular cytokine staining (ICC) (FIG. 5A).Memory cells from Gpr65^(−/−) mice that were reactivated with IL-23 alsoshowed a ˜45% reduction in IL-17a-positive cells compared to wild type(FIG. S6). Consistently, an enzyme-linked immunosorbent assay (ELISA) ofthe supernatant showed a reduced secretion of IL-17a (p<0.01) and IL-17f(p<2.5×10⁻⁵) (FIG. 5B) and increased IL-10 secretion (p<0.01, FIG. S6C)under pathogenic (IL-1β+IL-6+ L-23) differentiation conditions.

To further validate the effect of GPR65 on Th17 function, RNA-seqprofiles were measured of a bulk population of Gpr65^(−/−) Th17 cells,differentiated in vitro under TGF-β1+IL-6 for 96 hours. Supporting arole for GPR65 as a driver of pathogenicity of Th17 cells, it was foundthat genes up-regulated (compared to wild type) in Gpr65^(−/−) cells aresignificantly enriched (P<18.5×10^(−/−), hypergeometric test) for thegenes characterizing the more regulatory cells under TGF-β1+IL-6(positive PC1, FIG. 4C) and for genes down-regulated in thepathogenicity signature (Lee et al., 2012) (P<1.4λ10⁻⁴, hypergeometrictest).

To determine the effect of loss of GPR65 on tissue inflammation andautoimmune disease in vivo, CD4+ lymphocytes and splenocytes derivedfrom Gpr65^(−/−) mice were transferred into RAG-1^(−/−) mice followed byMOG₃₅₋₅₅ immunization. It was found that in the absence ofGPR65-expressing T cells, mice are protected from EAE (FIG. 5D) and farfewer IL-17A and IFN-γ positive cells are recovered from the LN andspleen compared to controls transferred with wild-type cells (FIG. S6B).Furthermore, in vitro restimulation of the spleen and LN cells from theimmunized mice with MOG₃₅_55 showed that loss of GPR65 resulted indramatic reduction of MOG-specific IL-17A or IFN-γ positive cellscompared to their wild-type controls (FIG. 5C), suggesting that GPR65regulates the generation of encephalitogenic T cells in vivo. Takentogether, the data strongly validates that GPR65 is a positive regulatorof the pathogenic Th17 phenotype, and its loss results in protectionfrom EAE.

REFERENCES

-   International Multiple Sclerosis Genetics, C., Wellcome Trust Case    Control, C., Sawcer, S., Hellenthal, G., Pirinen, M., Spencer, C.    C., Patsopoulos, N. A., Moutsianas, L., Dilthey, A., Su, Z., et al.    (2011). Genetic risk and a primary role for cell-mediated immune    mechanisms in multiple sclerosis. Nature 476, 214-219.-   International Genetics of Ankylosing Spondylitis, C., Cortes, A.,    Hadler, J., Pointon, J. P., Robinson, P. C., Karaderi, T., Leo, P.,    Cremin, K., Pryce, K., Harris, J., et al. (2013). Identification of    multiple risk variants for ankylosing spondylitis through    high-density genotyping of immune-related loci. Nature genetics 45,    730-738.-   Jostins, L., Ripke, S., Weersma, R. K., Duerr, R. H., McGovern, D.    P., Hui, K. Y., Lee, J. C., Schumm, L. P., Sharma, Y., Anderson, C.    A., et al. (2012). Host-microbe interactions have shaped the genetic    architecture of inflammatory bowel disease. Nature 491, 119-124.-   Franke, A., McGovern, D. P., Barrett, J. C., Wang, K.,    Radford-Smith, G. L., Ahmad, T., Lees, C. W., Balschun, T., Lee, J.,    Roberts, R., et al. (2010). Genome-wide meta-analysis increases to    71 the number of confirmed Crohn's disease susceptibility loci.    Nature genetics 42, 1118-1125.-   Lee, Y., Awasthi, A., Yosef, N., Quintana, F. J., Xiao, S., Peters,    A., Wu, C., Kleinewietfeld, M., Kunder, S., Hafler, D. A., et al.    (2012). Induction and molecular signature of pathogenic TH17 cells.    Nature immunology 13, 991-999.

Example 12: MOG-Stimulated Plzp^(−/−) Cells have a Defect in GeneratingPathogenic Th17 Cells

PLZP (ROG), a transcription factor, is a known repressor of GATA3 (Miawet al., 2000) (Th2 master regulator), and regulates cytokine expression(Miaw et al., 2000) in T-helper cells. Since Plzp is co-expressed withthe pro-inflammatory module, it was hypothesized that it may regulatepathogenicity in Th17 cells. (It was, however, not possible to undertakean EAE experiment since PLZP^(−/−) mice are not available on theEAE-susceptible background.)

While in vitro differentiated Plzp^(−/−) cells produced IL-17A atcomparable levels to wild-type (FIG. S8A), a MOG-driven recall assayrevealed that Plzp^(−/−) cells have a defect in IL-17A production thatbecomes apparent with increasing MOG concentration during restimulation(FIG. 5H). Furthermore, Plzp^(−/−) cells also produced less IL-17A thanwild-type cells when reactivated in the presence of IL-23, which acts toexpand previously in vivo differentiated Th17 cells (FIG. S8B). Finally,Plzp^(−/−) T cells secreted less IL-17A, IL-17F (FIG. 5I), IFN-γ, IL-13and GM-CSF (FIG. S8C). These observations suggest that PLZP regulatesthe expression of a wider range of inflammatory cytokines. At 48 hoursinto the differentiation of Plzp^(−/−) cells, Irf1 (FC=5.1), Il-9(FC=1.8) and other transcripts of the regulatory module are up regulatedcompared to WT (Table S10), whereas transcripts from thepro-inflammatory module, such as Ccl-20 (FC=0.38), Tnf (FC=0.10) andIl-17a (FC=0.42), are repressed.

Thus, by single cell genomics and covariance analysis, a number of novelregulators of pathogenicity of Th17 cells that affect development ofTh17 cells in vitro and autoimmunity in vivo have been identified.

REFERENCES

-   Miaw, S. C., Choi, A., Yu, E., Kishikawa, H., and Ho, I. C. (2000).    ROG, repressor of GATA, regulates the expression of cytokine genes.    Immunity 12, 323-333.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention.

1. A method of diagnosing, prognosing and/or staging an immune responseinvolving T cell balance, comprising detecting a first level ofexpression, activity and/or function of CD5L and comparing the detectedlevel to a control of level of CD5L expression, activity and/orfunction, wherein a difference in the detected level and the controllevel indicates that the presence of an immune response in the subject.2. The method of claim 1, wherein an immune response is monitored in asubject comprising detecting a level of expression, activity and/orfunction of CD5L at a first time point, detecting a level of expression,activity and/or function of CD5L at a second time point, and comparingthe first detected level of expression, activity and/or function withthe second detected level of expression, activity and/or function,wherein a change in the first and second detected levels indicates achange in the immune response in the subject.
 3. The method of claim 1,wherein a patient population at risk or suffering from an immuneresponse is identified comprising detecting a level of expression,activity and/or function of CD5L in the patient population and comparingthe level of expression, activity and/or function of CD5L in a patientpopulation not at risk or suffering from an immune response, wherein adifference in the level of expression, activity and/or function of CD5Lin the patient populations identifies the patient population as at riskor suffering from an immune response.
 4. A method for monitoringsubjects undergoing a treatment or therapy for an aberrant immuneresponse to determine whether the patient is responsive to the treatmentor therapy comprising detecting a level of expression, activity and/orfunction of IL17A in the absence of the treatment or therapy andcomparing the level of expression, activity and/or function of IL17A inthe presence of the treatment or therapy, wherein a difference in thelevel of expression, activity and/or function of IL17A in the presenceof the treatment or therapy indicates whether the patient is responsiveto the treatment or therapy, wherein the treatment or therapy isspecific for CD5L.
 5. The method of claim 1, wherein the immune responseis an autoimmune response or an inflammatory response.
 6. The method ofclaim 5 wherein the inflammatory response is associated with anautoimmune response, an infectious disease and/or a pathogen-baseddisorder.
 7. The method of claim 4, wherein the treatment or therapy isan antagonist of CD5L in an amount sufficient to switch Th17 cells froma non-pathogenic to pathogenic signature; or wherein the treatment ortherapy is an agonist that enhances or increases the expression of CD5Lin an amount sufficient to switch Th17 cells from a pathogenic to anon-pathogenic signature.
 8. The method according to claim 7, whereinthe treatment or therapy targets T cells and the T cells are naïve Tcells, partially differentiated T cells, differentiated T cells, acombination of naïve T cells and partially differentiated T cells, acombination of naïve T cells and differentiated T cells, a combinationof partially differentiated T cells and differentiated T cells, or acombination of naïve T cells, partially differentiated T cells anddifferentiated T cells.
 9. A method of modulating T cell balance, themethod comprising contacting a Th17 cell or a population of T cellscomprising Th17 cells or T cells capable of differentiating into Th17cells with an exogenous T cell modulating agent in an amount sufficientto modify maintenance and/or function of the Th17 cell or population ofT cells by altering balance between pathogenic and non-pathogenic Th17cells as compared to maintenance and/or function of the Th17 cell orpopulation of T cells in the absence of the T cell modulating agent,wherein the T cell modulating agent is specific for CD5L.
 10. The methodof claim 9, wherein the T cell modulating agent is an antagonist of CD5Lin an amount sufficient to switch Th17 cells from a non-pathogenic topathogenic signature.
 11. The method of claim 9, wherein the T cellmodulating agent is an agonist that enhances or increases the expressionof CD5L in an amount sufficient to switch Th17 cells from a pathogenicto non-pathogenic signature.
 12. The method according to claim 9,wherein the population of T cells comprise naïve T cells, partiallydifferentiated T cells, differentiated T cells, a combination of naïve Tcells and partially differentiated T cells, a combination of naïve Tcells and differentiated T cells, a combination of partiallydifferentiated T cells and differentiated T cells, or a combination ofnaïve T cells, partially differentiated T cells and differentiated Tcells.
 13. A method of drug discovery for the treatment of a disease orcondition involving an immune response involving Th17 cells comprisingthe steps of: (a) providing a population of cells or tissue comprisingTh17 cells; (b) providing a CD5L specific compound or plurality ofcompounds to be screened for their efficacy in the treatment of saiddisease or condition; (c) contacting said compound or plurality ofcompounds with said population of cells or tissue; (d) detecting a firstlevel of expression, activity and/or function of one or more signaturegenes or one or more products of one or more signature genes selectedfrom the genes of Table 1 or Table 2; (e) comparing the detected levelto a control of level of one or more signature genes or one or moreproducts of one or more signature genes selected from the genes of Table1 or Table 2 or gene product expression, activity and/or function; and,(e) evaluating the difference between the detected level and the controllevel to determine the immune response elicited by said CD5L specificcompound or plurality of compounds.